Job Description At Hexaware Technologies, we are a leading global IT Services company, dedicated to driving digital transformation and innovation for businesses around the world. Founded in 1990, Hexaware has grown into a globally trusted partner for enterprises, offering comprehensive AI empowered services including IT Consulting, Application Development, Infrastructure and Cloud Management and Business Process services. At Hexaware we are a community of creative, diverse, and open-minded Hexawarein creating smiles through the power of great people and technology. We pride ourselves on our people-centric culture and commitment to sustainability. Our diverse team of over 30,000 professionals across 30 countries is driven by a shared passion for innovation and excellence. We foster a collaborative environment where creativity and continuous learning are encouraged, enabling our employees to thrive and grow. Position: Collaboration Engineer Location: Florham Park, NJ 07932 Hiring: Fulltime with Hexaware Technologies. Key Responsibilities: Audio/Video & Meeting Rooms: Design, install, and maintain the AV setup in conference rooms, boardrooms, and huddle spaces - Microsoft Teams Rooms, Cisco Webex, and the Crestron and Extron gear behind them. Run day-to-day administration of the video conferencing tenant(s): provisioning, monitoring room health, and managing hardware through its lifecycle. Test and validate AV systems ahead of executive meetings, town halls, live webcasts, and other high-visibility events to catch issues before they surface and train the Service Desk team on first-line troubleshooting. Write and maintain the SOPs that keep AV deployments consistent from site to site. Telephony & Unified Communications: Keep our Unified Communications /telephony platforms running - Teams Phone, Cisco Call Manager while it's still in place, SIP trunking, Direct Routing/Operator Connect. Work alongside the Telecom Engineer on the voice migration - cutovers, dial plan changes, and decommissioning old systems. Test and validate UC/telephony changes and cutovers before go-live and troubleshoot call quality and connectivity issues as they come up. Help move life-safety and emergency POTS lines to POTS in a Box without losing E911 compliance along the way. Keep dial plans, configs, and telephony documentation current, and train the broader support team on telephony troubleshooting and escalation. Mobility & Device Management: Administer our MDM/MAM platform (Intune or similar) - provisioning, compliance, and security policy. Own the mobile and endpoint device lifecycle: ordering, provisioning, refresh cycles, and retirement. Track Telecom Expense Management - review carrier bills, catch overspend, find savings. Enforce device policy and monitor compliance across both corporate-owned and BYOD mobile devices. Train support staff on mobility troubleshooting and device management procedures. Vendor & Project Work: Coordinate directly with AV, UC, carrier, and mobility vendors on installs, refreshes, and service issues. Help build and track project plans and KPIs for AV, telephony, and mobility work. Flag and report cost savings as old systems and lines get retired. Collaboration: Work with the Collaboration & Digital Experience team so AV, telephony, and mobility tools fit cleanly into the Teams/Microsoft 365 experience. Coordinate with Networking on bandwidth and site readiness before AV/UC rollouts. Partner with the Service Desk Manager to equip the front-line support team with the documentation, training, and escalation paths they need for executive and leadership-facing issues. Qualifications: Bachelor's degree in IT, Telecommunications, or related field, or equivalent hands-on experience. 5+ years hands-on experience in enterprise AV, conferencing, and/or telephony. Real experience with room systems (Teams Rooms, Cisco Webex) and AV control gear (Crestron, Extron). Working knowledge of enterprise telephony/UC (Cisco Call Manager, Teams Phone, SIP trunking). Experience with MDM/MAM (Intune or similar) and device lifecycle management. Strong testing and validation discipline, with the ability to train and mentor other team members on AV, UC/telephony, and mobility systems. Strong vendor coordination and documentation skills. Able to travel to sites as needed for installs, events, and cutovers.
11/14/2026
Full time
Job Description At Hexaware Technologies, we are a leading global IT Services company, dedicated to driving digital transformation and innovation for businesses around the world. Founded in 1990, Hexaware has grown into a globally trusted partner for enterprises, offering comprehensive AI empowered services including IT Consulting, Application Development, Infrastructure and Cloud Management and Business Process services. At Hexaware we are a community of creative, diverse, and open-minded Hexawarein creating smiles through the power of great people and technology. We pride ourselves on our people-centric culture and commitment to sustainability. Our diverse team of over 30,000 professionals across 30 countries is driven by a shared passion for innovation and excellence. We foster a collaborative environment where creativity and continuous learning are encouraged, enabling our employees to thrive and grow. Position: Collaboration Engineer Location: Florham Park, NJ 07932 Hiring: Fulltime with Hexaware Technologies. Key Responsibilities: Audio/Video & Meeting Rooms: Design, install, and maintain the AV setup in conference rooms, boardrooms, and huddle spaces - Microsoft Teams Rooms, Cisco Webex, and the Crestron and Extron gear behind them. Run day-to-day administration of the video conferencing tenant(s): provisioning, monitoring room health, and managing hardware through its lifecycle. Test and validate AV systems ahead of executive meetings, town halls, live webcasts, and other high-visibility events to catch issues before they surface and train the Service Desk team on first-line troubleshooting. Write and maintain the SOPs that keep AV deployments consistent from site to site. Telephony & Unified Communications: Keep our Unified Communications /telephony platforms running - Teams Phone, Cisco Call Manager while it's still in place, SIP trunking, Direct Routing/Operator Connect. Work alongside the Telecom Engineer on the voice migration - cutovers, dial plan changes, and decommissioning old systems. Test and validate UC/telephony changes and cutovers before go-live and troubleshoot call quality and connectivity issues as they come up. Help move life-safety and emergency POTS lines to POTS in a Box without losing E911 compliance along the way. Keep dial plans, configs, and telephony documentation current, and train the broader support team on telephony troubleshooting and escalation. Mobility & Device Management: Administer our MDM/MAM platform (Intune or similar) - provisioning, compliance, and security policy. Own the mobile and endpoint device lifecycle: ordering, provisioning, refresh cycles, and retirement. Track Telecom Expense Management - review carrier bills, catch overspend, find savings. Enforce device policy and monitor compliance across both corporate-owned and BYOD mobile devices. Train support staff on mobility troubleshooting and device management procedures. Vendor & Project Work: Coordinate directly with AV, UC, carrier, and mobility vendors on installs, refreshes, and service issues. Help build and track project plans and KPIs for AV, telephony, and mobility work. Flag and report cost savings as old systems and lines get retired. Collaboration: Work with the Collaboration & Digital Experience team so AV, telephony, and mobility tools fit cleanly into the Teams/Microsoft 365 experience. Coordinate with Networking on bandwidth and site readiness before AV/UC rollouts. Partner with the Service Desk Manager to equip the front-line support team with the documentation, training, and escalation paths they need for executive and leadership-facing issues. Qualifications: Bachelor's degree in IT, Telecommunications, or related field, or equivalent hands-on experience. 5+ years hands-on experience in enterprise AV, conferencing, and/or telephony. Real experience with room systems (Teams Rooms, Cisco Webex) and AV control gear (Crestron, Extron). Working knowledge of enterprise telephony/UC (Cisco Call Manager, Teams Phone, SIP trunking). Experience with MDM/MAM (Intune or similar) and device lifecycle management. Strong testing and validation discipline, with the ability to train and mentor other team members on AV, UC/telephony, and mobility systems. Strong vendor coordination and documentation skills. Able to travel to sites as needed for installs, events, and cutovers.
Pay Range: $20.46hr - $25.46hr Job Overview: The AV/IT Event Operations Coordinator plays a key role in delivering exceptional experiences for employees and guests attending the Orlando campus. This role serves as a primary on-site resource for meeting support, guest services, audio visual technology support, and facility operations. The successful candidate will work closely with program sponsors, facilitators, employees, and campus vendors to create a welcoming, professional, and highly responsive environment. The campus consists of 3 training rooms (up to 30 people), 3 conference rooms (8 people), and 1 large caf (max seating 100 people), all equipped with built-in technology. Key Responsibilities A/V/Tech: Set-up and test built-in classroom Audio Visual equipment on-site for daily meetings. Provide AV assistance to coaches, clients, and facilitators. Troubleshoot technology challenges for employees and visiting guests/facilitators. Provide on-site technology support for Executive Meetings, both in-person and hybrid. Act as an on-site AV & Tech liaison for campus equipment, supporting troubleshooting with off-site IT/AV for printers, monitors, employee laptops, Macs, and internet testing. Run general equipment updates on campus technology. Familiarize and upskill on existing campus equipment & software, including Microsoft Office, Teams, Zoom, PowerPoint, Zoom/Teams enabled rooms with ceiling mics & cameras, projectors, microphones, Logitech Taps, and PC & Mac devices. Update and manage the campus SharePoint site. Edit & upload new content for digital signage on campus. Play an active role on team projects for campus AV & technology enhancements. Perform additional duties as assigned. Key Responsibilities Events: Serve as an on-site point of contact for meeting hosts, facilitators, and campus guests. Support the execution of meetings, workshops, and employee events. Assist with room setup, meeting readiness, and event logistics. Check on-site catering and partner with the on-site catering team regarding updates, changes, and requests. Participate in pre-planning prep calls as needed. Conduct safety briefings and housekeeping announcements for campus programs. Greet and direct guests, facilitators, and vendors throughout campus. Assist with participant materials, name badges, handouts, and program supplies. Provide printing, copying, and general administrative support for campus meetings and events. Maintain meeting rooms, common areas, and event spaces to a 'Show ready' level at all times. Proactively anticipate guest needs and respond to requests promptly and positively. Maintain compliance and safety across workplace standards. Perform other duties as assigned. Qualifications: Minimum 2 years of experience supporting Audio Visual technology, conference center operations, or event support. Proficiency with Microsoft Office, Teams, and related workplace technologies. Demonstrated ability to manage resources effectively. Comfortable maintaining a high level of interaction with executives, department heads, and strategic business partners. Demonstrated strong problem-solving skills as well as excellent verbal and written communication skills. Interest in technology and digital upskilling. Willingness to learn and integrate new technology. Ability to troubleshoot AV & IT equipment and find solutions while remaining calm in a high-pressure environment. Strong organizational skills. Understanding and respecting diverse cultures and backgrounds to provide inclusive service. Ability to work flexible hours on occasion. Physical Requirements(Due to nature of job, The candidate must have): Ability to lift up to 50 lbs. Ability to bend, walk, and stand for extended periods of time. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
11/14/2026
Full time
Pay Range: $20.46hr - $25.46hr Job Overview: The AV/IT Event Operations Coordinator plays a key role in delivering exceptional experiences for employees and guests attending the Orlando campus. This role serves as a primary on-site resource for meeting support, guest services, audio visual technology support, and facility operations. The successful candidate will work closely with program sponsors, facilitators, employees, and campus vendors to create a welcoming, professional, and highly responsive environment. The campus consists of 3 training rooms (up to 30 people), 3 conference rooms (8 people), and 1 large caf (max seating 100 people), all equipped with built-in technology. Key Responsibilities A/V/Tech: Set-up and test built-in classroom Audio Visual equipment on-site for daily meetings. Provide AV assistance to coaches, clients, and facilitators. Troubleshoot technology challenges for employees and visiting guests/facilitators. Provide on-site technology support for Executive Meetings, both in-person and hybrid. Act as an on-site AV & Tech liaison for campus equipment, supporting troubleshooting with off-site IT/AV for printers, monitors, employee laptops, Macs, and internet testing. Run general equipment updates on campus technology. Familiarize and upskill on existing campus equipment & software, including Microsoft Office, Teams, Zoom, PowerPoint, Zoom/Teams enabled rooms with ceiling mics & cameras, projectors, microphones, Logitech Taps, and PC & Mac devices. Update and manage the campus SharePoint site. Edit & upload new content for digital signage on campus. Play an active role on team projects for campus AV & technology enhancements. Perform additional duties as assigned. Key Responsibilities Events: Serve as an on-site point of contact for meeting hosts, facilitators, and campus guests. Support the execution of meetings, workshops, and employee events. Assist with room setup, meeting readiness, and event logistics. Check on-site catering and partner with the on-site catering team regarding updates, changes, and requests. Participate in pre-planning prep calls as needed. Conduct safety briefings and housekeeping announcements for campus programs. Greet and direct guests, facilitators, and vendors throughout campus. Assist with participant materials, name badges, handouts, and program supplies. Provide printing, copying, and general administrative support for campus meetings and events. Maintain meeting rooms, common areas, and event spaces to a 'Show ready' level at all times. Proactively anticipate guest needs and respond to requests promptly and positively. Maintain compliance and safety across workplace standards. Perform other duties as assigned. Qualifications: Minimum 2 years of experience supporting Audio Visual technology, conference center operations, or event support. Proficiency with Microsoft Office, Teams, and related workplace technologies. Demonstrated ability to manage resources effectively. Comfortable maintaining a high level of interaction with executives, department heads, and strategic business partners. Demonstrated strong problem-solving skills as well as excellent verbal and written communication skills. Interest in technology and digital upskilling. Willingness to learn and integrate new technology. Ability to troubleshoot AV & IT equipment and find solutions while remaining calm in a high-pressure environment. Strong organizational skills. Understanding and respecting diverse cultures and backgrounds to provide inclusive service. Ability to work flexible hours on occasion. Physical Requirements(Due to nature of job, The candidate must have): Ability to lift up to 50 lbs. Ability to bend, walk, and stand for extended periods of time. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
Yoh, A Day & Zimmermann Company
New York, New York
Job Description Solution Architect (Insurance - Group Benefits) Direct Hire Hybrid (NY/NJ/CT/PA) Seeking a Solution Architect to support a Group Benefits platform. This role is urgent and will prioritize candidates with recent, hands-on insurance domain experience and a proven track record architecting large-scale, business-critical systems in the insurance space-ideally tied to group benefits products and platforms. Work Location (Hybrid): 3 days/week onsite in New York, NY; Holmdel, NJ; Bethlehem, PA; or Stamford, CT Key Responsibilities Own product/team-level solution architecture and delivery architecture for a value stream supporting a Group Benefits insurance platform. Drive architecture & technology alignment-ensuring solutions map to business capabilities, product priorities, and platform direction. Design and document end-to-end solution architectures including application, integration, data, and infrastructure components. Partner with Product Owners and Tech Leads to align roadmaps, MVP scope, sequencing, and technical tradeoffs. Apply and enforce architecture standards, security requirements, and approved design patterns. Collaborate with architecture and platform teams to reuse enterprise capabilities and reduce duplication. Identify, manage, and help remove technical dependencies/impediments impacting delivery timelines. Provide hands-on guidance to teams on design quality, code structure, and implementation approaches. Lead proofs-of-concept for new capabilities; influence technology strategy, standards, and solution direction. Mentor engineers on architecture principles, design decisions, and problem-solving methods. Continuously improve platform design by reducing technical debt and simplifying solutions for scalability and delivery speed. Be available as needed for major production incidents impacting business operations. Required Qualifications Insurance domain experience is mandatory. Preference for Group Benefits (e.g., Life, Disability, Absence Management, Supplemental Health) platforms. Strong experience designing and delivering enterprise-scale solutions in complex environments. Demonstrated ability to architect systems that support high availability, resiliency, scalability, and operational excellence. Proven track record operating and evolving large-scale, business-critical systems within the insurance domain. Working knowledge of: Cloud platforms and services (public cloud + on-prem / hybrid) APIs, integration patterns, and data integration Modern application architecture approaches (service-based architectures, event-driven patterns, etc.) Experience working within Agile / product-based delivery models. Ability to balance speed, quality, risk, and long-term sustainability in technical decisions. Strong communication skills-able to translate technical concepts for product, engineering, and business stakeholders. Experience producing product-level and team-level design documentation. Familiarity with GenAI frameworks/tools and best practices (nice-to-have but valued Work Authorization Must be authorized to work in the United States. No sponsorship available. Estimated Min Rate : $126000.00 Estimated Max Rate : $180000.00 What's In It for You? We welcome you to be a part of the largest and legendary global staffing companies to meet your career aspirations. Yoh's network of client companies has been employing professionals like you for over 65 years in the U.S., UK and Canada. Join Yoh's extensive talent community that will provide you with access to Yoh's vast network of opportunities and gain access to this exclusive opportunity available to you. Benefit eligibility is in accordance with applicable laws and client requirements. Benefits include: Medical, Prescription, Dental & Vision Benefits (for employees working 20+ hours per week) Health Savings Account (HSA) (for employees working 20+ hours per week) Life & Disability Insurance (for employees working 20+ hours per week) MetLife Voluntary Benefits Employee Assistance Program (EAP) 401K Retirement Savings Plan Direct Deposit & weekly epayroll Referral Bonus Programs Certification and training opportunities Note: Any pay ranges displayed are estimations. Actual pay is determined by an applicant's experience, technical expertise, and other qualifications as listed in the job description. All qualified applicants are welcome to apply. Yoh, a Day & Zimmermann company, is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. Visit -with-disabilities to contact us if you are an individual with a disability and require accommodation in the application process. For California applicants, qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. All of the material job duties described in this posting are job duties for which a criminal history may have a direct, adverse, and negative relationship potentially resulting in the withdrawal of a conditional offer of employment. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. By applying and submitting your resume, you authorize Yoh to review and reformat your resume to meet Yoh's hiring clients' preferences. To learn more about Yoh's privacy practices, please see our Candidate Privacy Notice: -notice Stay Safe During Your Job Search Fraudulent recruiting communications have become increasingly common. Emails from Yoh recruiters will only come from email address. Yoh will never ask candidates to pay fees, purchase equipment, send gift cards, or transfer funds as part of the recruiting or hiring process. If you receive a communication that appears suspicious or requests payment, contact Yoh directly before responding or sharing personal information.
11/14/2026
Full time
Job Description Solution Architect (Insurance - Group Benefits) Direct Hire Hybrid (NY/NJ/CT/PA) Seeking a Solution Architect to support a Group Benefits platform. This role is urgent and will prioritize candidates with recent, hands-on insurance domain experience and a proven track record architecting large-scale, business-critical systems in the insurance space-ideally tied to group benefits products and platforms. Work Location (Hybrid): 3 days/week onsite in New York, NY; Holmdel, NJ; Bethlehem, PA; or Stamford, CT Key Responsibilities Own product/team-level solution architecture and delivery architecture for a value stream supporting a Group Benefits insurance platform. Drive architecture & technology alignment-ensuring solutions map to business capabilities, product priorities, and platform direction. Design and document end-to-end solution architectures including application, integration, data, and infrastructure components. Partner with Product Owners and Tech Leads to align roadmaps, MVP scope, sequencing, and technical tradeoffs. Apply and enforce architecture standards, security requirements, and approved design patterns. Collaborate with architecture and platform teams to reuse enterprise capabilities and reduce duplication. Identify, manage, and help remove technical dependencies/impediments impacting delivery timelines. Provide hands-on guidance to teams on design quality, code structure, and implementation approaches. Lead proofs-of-concept for new capabilities; influence technology strategy, standards, and solution direction. Mentor engineers on architecture principles, design decisions, and problem-solving methods. Continuously improve platform design by reducing technical debt and simplifying solutions for scalability and delivery speed. Be available as needed for major production incidents impacting business operations. Required Qualifications Insurance domain experience is mandatory. Preference for Group Benefits (e.g., Life, Disability, Absence Management, Supplemental Health) platforms. Strong experience designing and delivering enterprise-scale solutions in complex environments. Demonstrated ability to architect systems that support high availability, resiliency, scalability, and operational excellence. Proven track record operating and evolving large-scale, business-critical systems within the insurance domain. Working knowledge of: Cloud platforms and services (public cloud + on-prem / hybrid) APIs, integration patterns, and data integration Modern application architecture approaches (service-based architectures, event-driven patterns, etc.) Experience working within Agile / product-based delivery models. Ability to balance speed, quality, risk, and long-term sustainability in technical decisions. Strong communication skills-able to translate technical concepts for product, engineering, and business stakeholders. Experience producing product-level and team-level design documentation. Familiarity with GenAI frameworks/tools and best practices (nice-to-have but valued Work Authorization Must be authorized to work in the United States. No sponsorship available. Estimated Min Rate : $126000.00 Estimated Max Rate : $180000.00 What's In It for You? We welcome you to be a part of the largest and legendary global staffing companies to meet your career aspirations. Yoh's network of client companies has been employing professionals like you for over 65 years in the U.S., UK and Canada. Join Yoh's extensive talent community that will provide you with access to Yoh's vast network of opportunities and gain access to this exclusive opportunity available to you. Benefit eligibility is in accordance with applicable laws and client requirements. Benefits include: Medical, Prescription, Dental & Vision Benefits (for employees working 20+ hours per week) Health Savings Account (HSA) (for employees working 20+ hours per week) Life & Disability Insurance (for employees working 20+ hours per week) MetLife Voluntary Benefits Employee Assistance Program (EAP) 401K Retirement Savings Plan Direct Deposit & weekly epayroll Referral Bonus Programs Certification and training opportunities Note: Any pay ranges displayed are estimations. Actual pay is determined by an applicant's experience, technical expertise, and other qualifications as listed in the job description. All qualified applicants are welcome to apply. Yoh, a Day & Zimmermann company, is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. Visit -with-disabilities to contact us if you are an individual with a disability and require accommodation in the application process. For California applicants, qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. All of the material job duties described in this posting are job duties for which a criminal history may have a direct, adverse, and negative relationship potentially resulting in the withdrawal of a conditional offer of employment. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. By applying and submitting your resume, you authorize Yoh to review and reformat your resume to meet Yoh's hiring clients' preferences. To learn more about Yoh's privacy practices, please see our Candidate Privacy Notice: -notice Stay Safe During Your Job Search Fraudulent recruiting communications have become increasingly common. Emails from Yoh recruiters will only come from email address. Yoh will never ask candidates to pay fees, purchase equipment, send gift cards, or transfer funds as part of the recruiting or hiring process. If you receive a communication that appears suspicious or requests payment, contact Yoh directly before responding or sharing personal information.
Pay Range: $20.46hr - $25.46hr Job Overview: The AV/IT Event Operations Coordinator plays a key role in delivering exceptional experiences for employees and guests attending the Orlando campus. This role serves as a primary on-site resource for meeting support, guest services, audio visual technology support, and facility operations. The successful candidate will work closely with program sponsors, facilitators, employees, and campus vendors to create a welcoming, professional, and highly responsive environment. The campus consists of 3 training rooms (up to 30 people), 3 conference rooms (8 people), and 1 large caf (max seating 100 people), all equipped with built-in technology. Key Responsibilities A/V/Tech: Set-up and test built-in classroom Audio Visual equipment on-site for daily meetings. Provide AV assistance to coaches, clients, and facilitators. Troubleshoot technology challenges for employees and visiting guests/facilitators. Provide on-site technology support for Executive Meetings, both in-person and hybrid. Act as an on-site AV & Tech liaison for campus equipment, supporting troubleshooting with off-site IT/AV for printers, monitors, employee laptops, Macs, and internet testing. Run general equipment updates on campus technology. Familiarize and upskill on existing campus equipment & software, including Microsoft Office, Teams, Zoom, PowerPoint, Zoom/Teams enabled rooms with ceiling mics & cameras, projectors, microphones, Logitech Taps, and PC & Mac devices. Update and manage the campus SharePoint site. Edit & upload new content for digital signage on campus. Play an active role on team projects for campus AV & technology enhancements. Perform additional duties as assigned. Key Responsibilities Events: Serve as an on-site point of contact for meeting hosts, facilitators, and campus guests. Support the execution of meetings, workshops, and employee events. Assist with room setup, meeting readiness, and event logistics. Check on-site catering and partner with the on-site catering team regarding updates, changes, and requests. Participate in pre-planning prep calls as needed. Conduct safety briefings and housekeeping announcements for campus programs. Greet and direct guests, facilitators, and vendors throughout campus. Assist with participant materials, name badges, handouts, and program supplies. Provide printing, copying, and general administrative support for campus meetings and events. Maintain meeting rooms, common areas, and event spaces to a 'Show ready' level at all times. Proactively anticipate guest needs and respond to requests promptly and positively. Maintain compliance and safety across workplace standards. Perform other duties as assigned. Qualifications: Minimum 2 years of experience supporting Audio Visual technology, conference center operations, or event support. Proficiency with Microsoft Office, Teams, and related workplace technologies. Demonstrated ability to manage resources effectively. Comfortable maintaining a high level of interaction with executives, department heads, and strategic business partners. Demonstrated strong problem-solving skills as well as excellent verbal and written communication skills. Interest in technology and digital upskilling. Willingness to learn and integrate new technology. Ability to troubleshoot AV & IT equipment and find solutions while remaining calm in a high-pressure environment. Strong organizational skills. Understanding and respecting diverse cultures and backgrounds to provide inclusive service. Ability to work flexible hours on occasion. Physical Requirements(Due to nature of job, The candidate must have): Ability to lift up to 50 lbs. Ability to bend, walk, and stand for extended periods of time. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
11/14/2026
Full time
Pay Range: $20.46hr - $25.46hr Job Overview: The AV/IT Event Operations Coordinator plays a key role in delivering exceptional experiences for employees and guests attending the Orlando campus. This role serves as a primary on-site resource for meeting support, guest services, audio visual technology support, and facility operations. The successful candidate will work closely with program sponsors, facilitators, employees, and campus vendors to create a welcoming, professional, and highly responsive environment. The campus consists of 3 training rooms (up to 30 people), 3 conference rooms (8 people), and 1 large caf (max seating 100 people), all equipped with built-in technology. Key Responsibilities A/V/Tech: Set-up and test built-in classroom Audio Visual equipment on-site for daily meetings. Provide AV assistance to coaches, clients, and facilitators. Troubleshoot technology challenges for employees and visiting guests/facilitators. Provide on-site technology support for Executive Meetings, both in-person and hybrid. Act as an on-site AV & Tech liaison for campus equipment, supporting troubleshooting with off-site IT/AV for printers, monitors, employee laptops, Macs, and internet testing. Run general equipment updates on campus technology. Familiarize and upskill on existing campus equipment & software, including Microsoft Office, Teams, Zoom, PowerPoint, Zoom/Teams enabled rooms with ceiling mics & cameras, projectors, microphones, Logitech Taps, and PC & Mac devices. Update and manage the campus SharePoint site. Edit & upload new content for digital signage on campus. Play an active role on team projects for campus AV & technology enhancements. Perform additional duties as assigned. Key Responsibilities Events: Serve as an on-site point of contact for meeting hosts, facilitators, and campus guests. Support the execution of meetings, workshops, and employee events. Assist with room setup, meeting readiness, and event logistics. Check on-site catering and partner with the on-site catering team regarding updates, changes, and requests. Participate in pre-planning prep calls as needed. Conduct safety briefings and housekeeping announcements for campus programs. Greet and direct guests, facilitators, and vendors throughout campus. Assist with participant materials, name badges, handouts, and program supplies. Provide printing, copying, and general administrative support for campus meetings and events. Maintain meeting rooms, common areas, and event spaces to a 'Show ready' level at all times. Proactively anticipate guest needs and respond to requests promptly and positively. Maintain compliance and safety across workplace standards. Perform other duties as assigned. Qualifications: Minimum 2 years of experience supporting Audio Visual technology, conference center operations, or event support. Proficiency with Microsoft Office, Teams, and related workplace technologies. Demonstrated ability to manage resources effectively. Comfortable maintaining a high level of interaction with executives, department heads, and strategic business partners. Demonstrated strong problem-solving skills as well as excellent verbal and written communication skills. Interest in technology and digital upskilling. Willingness to learn and integrate new technology. Ability to troubleshoot AV & IT equipment and find solutions while remaining calm in a high-pressure environment. Strong organizational skills. Understanding and respecting diverse cultures and backgrounds to provide inclusive service. Ability to work flexible hours on occasion. Physical Requirements(Due to nature of job, The candidate must have): Ability to lift up to 50 lbs. Ability to bend, walk, and stand for extended periods of time. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
Job Description Description Looking for an opportunity to make an impact? At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers' success. We empower our teams, contribute to our communities, and operate sustainable. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community. Our Mission, Vision, and Values guide the way we do business. Are you ready for your next career challenge? Leidos Defense provides a diverse portfolio of systems, solutions, and services covering land, sea, air, space, and cyberspace for customers worldwide. Solutions for Defense include enterprise and mission IT, large-scale intelligence systems, command and control, geospatial and data analytics, cybersecurity, logistics, training, and intelligence analysis and operations support. Our team is solving the world's toughest security challenges for customers with "can't fail" missions. Following NASA's award of the High Achievability Lunar Terrain Vehicle (HALTV) program contract to Lunar Outpost, Leidos, as a key partner, is seeking RF Communications Engineer to support HALTV and other programs as part of a diverse team that develops lunar mobility systems, cislunar spacecraft, and a wide range of critical technologies for civil, military, and commercial space programs. Primary Responsibilities The Lead RF Communications Engineer will support the Leidos Space Platforms Portfolio in the development of a lunar terrain vehicle system and support equipment, primarily responsible for subsystem integration, qualification, and testing of RF communications hardware into a seamless operational system. This position requires expertise in RF simulation analysis, subsystem-level testing, electromagnetic compatibility testing, and end-to-end system verification. The role also requires the ability to translate high-level product development strategies into system requirement specifications and to work with multi-discipline engineering teams to develop lower-level detailed requirements. Candidates will work in a team environment to support design through product implementation and should be able to contribute to engineering design dispositions and support resolution of documentation discrepancies in coordination with Lunar Outpost and other team members. Candidate may be required to travel as necessary (10-20%) Basic Qualifications Candidates must possess a bachelor's degree or master's degree in Electrical Engineering, Computer Engineering or equivalent degree from an ABET-accredited institution. Candidates with master's and PhD degrees are encouraged to apply, with degree level substitution for direct industry work experience. Candidates must have 2+ years of experience with roles, as detailed in this description, with an engineering company or system integrator where design, integration, testing, and/or modification of human-rated aerospace systems communications hardware was involved. Candidate must have demonstrated experience developing RF link budgets along with hardware experience with space-based transceivers, antennas, and amplifiers. Candidate must have hands-on experience with RF simulation and analysis tools such as HFSS, CST Studio Suite, ADS, or equivalent electromagnetic simulation software. Candidate must have excellent oral and written communication skills. Candidates must have demonstrated experience effectively communicating within a team environment. Candidate must be a resident of the North Alabama area or willing to relocate to the Huntsville - North Alabama, AL area. Preferred Qualifications Preferred candidates may have experience and/or working knowledge of the following: Space Communications Systems Demonstrated the ability to architect and implement complex space-based RF communication links/systems from concept through flight operations. Proficiency with CCSDS (Consultative Committee for Space Data Systems) standards and NASA communication protocols. Understanding of communications with regards to navigation and telemetry systems. Cislunar or deep space communications heritage. Experience with NASA Deep Space Network (DSN) or Near Space Network (NSN) operations. Wi-Fi and Commercial Wireless Systems Experience with Wi-Fi standards including IEEE 802.11n, 802.11ac, and 802.11ax (Wi-Fi 6) protocols and implementations. Hands-on experience with integration and testing of Wi-Fi subsystems in aerospace or ruggedized environments. Knowledge of Wi-Fi performance tests including throughput, latency, packet error rate, and range testing. Familiarity with Wi-Fi security protocols (WPA2, WPA3) and network configuration for mission-critical applications. Experience conducting Wi-Fi subsystem qualification testing including environmental testing (thermal, vibration, vacuum). RF Design and Analysis Experience with RF PCB design using tools such as Altium Designer or Keysight ADS. Proficiency in STK (Systems Tool Kit) integration and scripting for mission analysis and end-to-end system modeling. Coordinating and planning frequency spectrum with Federal Communications Commission (FCC), Interagency Operations Advisory Group (IOAG), National Telecommunications and Information Administration (NTIA), Space Frequency Coordination Group (SFCG), and International Telecommunication Union (ITU). Knowledgeable with electromagnetic interference (EMI) analysis and testing, passive intermodulation (PIM), and harmonic effects. Experience with cosite analysis and antenna pattern analysis using computational electromagnetic software such as STK, HFSS, E3Expert, GEMACS, and/or MATLAB/Simulink. Experience across multiple frequency bands including S-Band, X-Band, Ka-Band, C-Band, L-Band, and ISM band (2.4 GHz, 5 GHz) RF design. Knowledge of RF filter design (microstrip and waveguide). Experience with test bench equipment including VNA, VSG, oscilloscopes, spectrum analyzers, and Wi-Fi test equipment. SECURITY REQ: Candidate must be a US Citizen. Candidate must possess (and be able to maintain) a Final Secret Clearance or meet the eligibility to obtain (and maintain) a Secret Clearance If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares. Original Posting:November 9, 2026 For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above. Pay Range:Pay Range $69,550.00 - $125,725.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. About Leidos Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit . Pay and Benefits Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at -benefits. Securing Your Data Beware of fake employment opportunities using Leidos' name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the automated system - never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at . If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy . click apply for full job details
11/14/2026
Full time
Job Description Description Looking for an opportunity to make an impact? At Leidos, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customers' success. We empower our teams, contribute to our communities, and operate sustainable. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community. Our Mission, Vision, and Values guide the way we do business. Are you ready for your next career challenge? Leidos Defense provides a diverse portfolio of systems, solutions, and services covering land, sea, air, space, and cyberspace for customers worldwide. Solutions for Defense include enterprise and mission IT, large-scale intelligence systems, command and control, geospatial and data analytics, cybersecurity, logistics, training, and intelligence analysis and operations support. Our team is solving the world's toughest security challenges for customers with "can't fail" missions. Following NASA's award of the High Achievability Lunar Terrain Vehicle (HALTV) program contract to Lunar Outpost, Leidos, as a key partner, is seeking RF Communications Engineer to support HALTV and other programs as part of a diverse team that develops lunar mobility systems, cislunar spacecraft, and a wide range of critical technologies for civil, military, and commercial space programs. Primary Responsibilities The Lead RF Communications Engineer will support the Leidos Space Platforms Portfolio in the development of a lunar terrain vehicle system and support equipment, primarily responsible for subsystem integration, qualification, and testing of RF communications hardware into a seamless operational system. This position requires expertise in RF simulation analysis, subsystem-level testing, electromagnetic compatibility testing, and end-to-end system verification. The role also requires the ability to translate high-level product development strategies into system requirement specifications and to work with multi-discipline engineering teams to develop lower-level detailed requirements. Candidates will work in a team environment to support design through product implementation and should be able to contribute to engineering design dispositions and support resolution of documentation discrepancies in coordination with Lunar Outpost and other team members. Candidate may be required to travel as necessary (10-20%) Basic Qualifications Candidates must possess a bachelor's degree or master's degree in Electrical Engineering, Computer Engineering or equivalent degree from an ABET-accredited institution. Candidates with master's and PhD degrees are encouraged to apply, with degree level substitution for direct industry work experience. Candidates must have 2+ years of experience with roles, as detailed in this description, with an engineering company or system integrator where design, integration, testing, and/or modification of human-rated aerospace systems communications hardware was involved. Candidate must have demonstrated experience developing RF link budgets along with hardware experience with space-based transceivers, antennas, and amplifiers. Candidate must have hands-on experience with RF simulation and analysis tools such as HFSS, CST Studio Suite, ADS, or equivalent electromagnetic simulation software. Candidate must have excellent oral and written communication skills. Candidates must have demonstrated experience effectively communicating within a team environment. Candidate must be a resident of the North Alabama area or willing to relocate to the Huntsville - North Alabama, AL area. Preferred Qualifications Preferred candidates may have experience and/or working knowledge of the following: Space Communications Systems Demonstrated the ability to architect and implement complex space-based RF communication links/systems from concept through flight operations. Proficiency with CCSDS (Consultative Committee for Space Data Systems) standards and NASA communication protocols. Understanding of communications with regards to navigation and telemetry systems. Cislunar or deep space communications heritage. Experience with NASA Deep Space Network (DSN) or Near Space Network (NSN) operations. Wi-Fi and Commercial Wireless Systems Experience with Wi-Fi standards including IEEE 802.11n, 802.11ac, and 802.11ax (Wi-Fi 6) protocols and implementations. Hands-on experience with integration and testing of Wi-Fi subsystems in aerospace or ruggedized environments. Knowledge of Wi-Fi performance tests including throughput, latency, packet error rate, and range testing. Familiarity with Wi-Fi security protocols (WPA2, WPA3) and network configuration for mission-critical applications. Experience conducting Wi-Fi subsystem qualification testing including environmental testing (thermal, vibration, vacuum). RF Design and Analysis Experience with RF PCB design using tools such as Altium Designer or Keysight ADS. Proficiency in STK (Systems Tool Kit) integration and scripting for mission analysis and end-to-end system modeling. Coordinating and planning frequency spectrum with Federal Communications Commission (FCC), Interagency Operations Advisory Group (IOAG), National Telecommunications and Information Administration (NTIA), Space Frequency Coordination Group (SFCG), and International Telecommunication Union (ITU). Knowledgeable with electromagnetic interference (EMI) analysis and testing, passive intermodulation (PIM), and harmonic effects. Experience with cosite analysis and antenna pattern analysis using computational electromagnetic software such as STK, HFSS, E3Expert, GEMACS, and/or MATLAB/Simulink. Experience across multiple frequency bands including S-Band, X-Band, Ka-Band, C-Band, L-Band, and ISM band (2.4 GHz, 5 GHz) RF design. Knowledge of RF filter design (microstrip and waveguide). Experience with test bench equipment including VNA, VSG, oscilloscopes, spectrum analyzers, and Wi-Fi test equipment. SECURITY REQ: Candidate must be a US Citizen. Candidate must possess (and be able to maintain) a Final Secret Clearance or meet the eligibility to obtain (and maintain) a Secret Clearance If you're looking for comfort, keep scrolling. At Leidos, we outthink, outbuild, and outpace the status quo - because the mission demands it. We're not hiring followers. We're recruiting the ones who disrupt, provoke, and refuse to fail. Step 10 is ancient history. We're already at step 30 - and moving faster than anyone else dares. Original Posting:November 9, 2026 For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above. Pay Range:Pay Range $69,550.00 - $125,725.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. About Leidos Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. Headquartered in Reston, Virginia, with 47,000 global employees, Leidos reported annual revenues of approximately $16.7 billion for the fiscal year ended January 3, 2025. For more information, visit . Pay and Benefits Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available at -benefits. Securing Your Data Beware of fake employment opportunities using Leidos' name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the automated system - never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at . If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy . click apply for full job details
Job Description Requisition #: 2 Employment Type: Contract Labor Job Title: CL - Space Force - Network System Engineer Location: US-AZ-Scottsdale Overview: Our client engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. With a global team of 12,000+ top professionals, we partner with the best in industry to expand the bounds of innovation in the defense and scientific arenas. Given the nature of our work and who we are, we value trust, honesty, alignment and transparency. We offer highly competitive benefits and pride ourselves in being a great place to work with a shared sense of purpose. You will also enjoy a flexible work environment where contributions are recognized and rewarded. If who we are and what we do resonates with you, we invite you to join our high-performance team! Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans Responsibilities: Bachelor's degree in Engineering, or a related Science or Mathematics field, plus a minimum of 5 years of relevant experience; or Master's degree plus a minimum of 3 years of relevant experience. CLEARANCE REQUIREMENTS: Department of Defense Secret security clearance is required at time of hire. Applicants selected may be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. Due to the nature of work performed within our facilities, U.S. citizenship is required. Qualifications: Our client is currently seeking a Senior Network Design Engineer in our Scottsdale, AZ location. ROLE AND POSITION OBJECTIVES: As a Network Engineering Support for Satellite Ground Systems, you'll be a member of a cross functional team. Responsibilities of this role include requirements analysis, design, documentation, implementation, requirements verification, maintenance, and troubleshooting functions for data communications networks in support of the SDA program. This position requires travel to deployment sites in the US. We encourage you to apply if you have any of these preferred skills or experiences: Enterprise Network Design/topology High speed network design and implementation Juniper Networks Certified Administrator (JNCIA-Junos, JNCIPis preferred or Cisco CCNA certification , CCNP preferred Proficiency with Juniper SRX-series of firewall devices Maintenance of Network topology that utilize Linux IDM/Windows Servers for DNS and Account Management, Juniper Firewalls, Arista Switches and NAS/SAN storage Familiarity with Multicast, and routing protocols (BGP, OSPF, etc) Experience with vulnerability scanning, hardening, and resolution of findings Written skills to include development and maintenance of documentation for network, system, operations, etc. Familiarity with public cloud computing networking and services, including AWS Familiarity with VMware vSphere and virtual machines Familiarity with Red Hat Enterprise Linux operating system Primary duties: Documents network plans and designs, installs components, and configures services to meet project requirements. Analyze network topologies, traffic, and capacity requirements for monitoring, maintenance, and troubleshooting during events. Between project execution events, and as part of the network team, responsible for operations, maintenance, and troubleshooting of networks of varying classifications, to include implementing security configurations, performing network diagnostics, identifying required corrective actions, and documenting changes in a clear and concise manner. Physically install devices in equipment racks and user workspaces. Position requires extensive work at a computer in an office environment with additional work possible in a raised floor computing environment. Position may require extensive travel to deployment sites in US and abroad (US Passport required) What sets you apart: Creative thinker with demonstrated success leading the software development life cycle Collaborative team player eager to provide technical leadership and position others for success Exceptional communication and presentation skills, with notable record of securing buy-in on concepts and ideas Experienced in the art of negotiation with ability to meet challenging customer requirements with win-win solutions Commitment to ongoing professional development for yourself and others Identifies opportunities to apply AI for continuous improvement and innovation Six (6) years' experience integrating and configuring switches, routers, firewalls and gateways in data communications systems with IP version 4 and associated TCP and UDP protocols preferred Possess experience in at least two (2) of the following topics with proven demonstrated experience within the past five (5) years: One (1) year experience as a computer systems manager/operator on Microsoft or LINUX based computing environments, employing Windows Server 2012 (or newer), client Windows 7 (or newer), and LINUX operating systems; One (1) year experience managing and working on local area network (LAN) administration, operation, and architecture with a minimum of 30 client systems; Specific working experience of management of a mixture of Windows and LINUX computing environments; Must be familiar with virtualization techniques - simulating a hardware platform, operating system (OS), storage device, or network resources. Active/Current DoD Secret Clearance. Certified DOD 8570.1-M IAT Level 2 preferred. Workplace Options: This position is fully on-site. While on-site, you will be a part of the Scottsdale, AZ facility. Travel up to 25% is required for this role. Expect travel needs to be in surges during times of site equipment deployments. Frequent travel to one or both of these locations: OC-N: Operations Center-North (located at Grand Forks Air Force Base, North Dakota). OC-S: Operations Center-South (located at Redstone Arsenal, Alabama).
11/14/2026
Full time
Job Description Requisition #: 2 Employment Type: Contract Labor Job Title: CL - Space Force - Network System Engineer Location: US-AZ-Scottsdale Overview: Our client engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. With a global team of 12,000+ top professionals, we partner with the best in industry to expand the bounds of innovation in the defense and scientific arenas. Given the nature of our work and who we are, we value trust, honesty, alignment and transparency. We offer highly competitive benefits and pride ourselves in being a great place to work with a shared sense of purpose. You will also enjoy a flexible work environment where contributions are recognized and rewarded. If who we are and what we do resonates with you, we invite you to join our high-performance team! Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans Responsibilities: Bachelor's degree in Engineering, or a related Science or Mathematics field, plus a minimum of 5 years of relevant experience; or Master's degree plus a minimum of 3 years of relevant experience. CLEARANCE REQUIREMENTS: Department of Defense Secret security clearance is required at time of hire. Applicants selected may be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. Due to the nature of work performed within our facilities, U.S. citizenship is required. Qualifications: Our client is currently seeking a Senior Network Design Engineer in our Scottsdale, AZ location. ROLE AND POSITION OBJECTIVES: As a Network Engineering Support for Satellite Ground Systems, you'll be a member of a cross functional team. Responsibilities of this role include requirements analysis, design, documentation, implementation, requirements verification, maintenance, and troubleshooting functions for data communications networks in support of the SDA program. This position requires travel to deployment sites in the US. We encourage you to apply if you have any of these preferred skills or experiences: Enterprise Network Design/topology High speed network design and implementation Juniper Networks Certified Administrator (JNCIA-Junos, JNCIPis preferred or Cisco CCNA certification , CCNP preferred Proficiency with Juniper SRX-series of firewall devices Maintenance of Network topology that utilize Linux IDM/Windows Servers for DNS and Account Management, Juniper Firewalls, Arista Switches and NAS/SAN storage Familiarity with Multicast, and routing protocols (BGP, OSPF, etc) Experience with vulnerability scanning, hardening, and resolution of findings Written skills to include development and maintenance of documentation for network, system, operations, etc. Familiarity with public cloud computing networking and services, including AWS Familiarity with VMware vSphere and virtual machines Familiarity with Red Hat Enterprise Linux operating system Primary duties: Documents network plans and designs, installs components, and configures services to meet project requirements. Analyze network topologies, traffic, and capacity requirements for monitoring, maintenance, and troubleshooting during events. Between project execution events, and as part of the network team, responsible for operations, maintenance, and troubleshooting of networks of varying classifications, to include implementing security configurations, performing network diagnostics, identifying required corrective actions, and documenting changes in a clear and concise manner. Physically install devices in equipment racks and user workspaces. Position requires extensive work at a computer in an office environment with additional work possible in a raised floor computing environment. Position may require extensive travel to deployment sites in US and abroad (US Passport required) What sets you apart: Creative thinker with demonstrated success leading the software development life cycle Collaborative team player eager to provide technical leadership and position others for success Exceptional communication and presentation skills, with notable record of securing buy-in on concepts and ideas Experienced in the art of negotiation with ability to meet challenging customer requirements with win-win solutions Commitment to ongoing professional development for yourself and others Identifies opportunities to apply AI for continuous improvement and innovation Six (6) years' experience integrating and configuring switches, routers, firewalls and gateways in data communications systems with IP version 4 and associated TCP and UDP protocols preferred Possess experience in at least two (2) of the following topics with proven demonstrated experience within the past five (5) years: One (1) year experience as a computer systems manager/operator on Microsoft or LINUX based computing environments, employing Windows Server 2012 (or newer), client Windows 7 (or newer), and LINUX operating systems; One (1) year experience managing and working on local area network (LAN) administration, operation, and architecture with a minimum of 30 client systems; Specific working experience of management of a mixture of Windows and LINUX computing environments; Must be familiar with virtualization techniques - simulating a hardware platform, operating system (OS), storage device, or network resources. Active/Current DoD Secret Clearance. Certified DOD 8570.1-M IAT Level 2 preferred. Workplace Options: This position is fully on-site. While on-site, you will be a part of the Scottsdale, AZ facility. Travel up to 25% is required for this role. Expect travel needs to be in surges during times of site equipment deployments. Frequent travel to one or both of these locations: OC-N: Operations Center-North (located at Grand Forks Air Force Base, North Dakota). OC-S: Operations Center-South (located at Redstone Arsenal, Alabama).
Job Description Requisition #: 0 Employment Type: Contract Labor Job Title: CL - Space Force - Systems Network Engineer Location: US-AZ-Scottsdale Overview: Our client engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. With a global team of 12,000+ top professionals, we partner with the best in industry to expand the bounds of innovation in the defense and scientific arenas. Given the nature of our work and who we are, we value trust, honesty, alignment and transparency. We offer highly competitive benefits and pride ourselves in being a great place to work with a shared sense of purpose. You will also enjoy a flexible work environment where contributions are recognized and rewarded. If who we are and what we do resonates with you, we invite you to join our high-performance team! Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans Responsibilities: Bachelor's degree in Engineering, or a related Science or Mathematics field, plus a minimum of 8 years of relevant experience; or Master's degree plus a minimum of 6 years of relevant experience. CLEARANCE REQUIREMENTS: Department of Defense Secret security clearance is preferred at time of hire. Candidates must be able to obtain a Secret clearance within a reasonable amount of time from date of hire. Applicants selected may be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. Due to the nature of work performed within our facilities, U.S. citizenship is required. Qualifications: Our client is currently seeking a Systems Network Engineer in our Scottsdale, AZ facility. As a Space Force Systems Network Engineer, you'll design and plan network systems by providing specifications and detailed schematics for network architecture. You'll apply your extensive technical expertise to provide specific, detailed information for hardware and software selection, implementation techniques, and tools for the most efficient solution to meet business needs, including present and future capacity requirements. This is your chance to not just work with the best technologies, but actually help create them. Think you're up to the task? What you'll experience: Technologies that aren't just top-notch, they're often top-secret A team of bold thinkers committed to exploring what's next Opportunities to gain new knowledge - as it's discovered What you bring to the table: A Bachelor's degree in Engineering, a related specialized area or field is required (or equivalent experience) plus a minimum of 15 years of relevant experience in large networking systems Technical expertise in all areas of network and computer hardware and software interconnection and interfacing, such as routers, multiplexers, hubs, bridges and gateways, etc. Subject Matter Expert (SME) in at least one discipline/field with a working knowledge in multiple adjacent disciplines/fields Represent organization as prime technical contact on contracts and projects What sets you apart: Space and Ground Mission Experience Strong ability to develop solutions to highly complex problems that require significant ingenuity, innovation and creativity Space and Ground Mission Experience AWS Solutions Architect, Advanced Networking, Security Specialty, or equivalent cloud certification Cisco CCNP, Juniper JNCIP/JNCIE, Palo Alto, or comparable network/security certification.Experience with AWS SC2S, GovCloud, Direct Connect, Transit Gateway, and hybrid-cloud architectures Experience with Terraform, CloudFormation, Ansible, Python, or other automation/IaC tools Experience with SDA, PWSA, GMI, satellite ground systems, mission networks, or Space Vehicle Vendor integration.Experience with IPv6 design, implementation, and transition planning in enterprise or DoD environments Ability to lead a team and mentor staff Strong independent judgement Identifies opportunities to apply AI for continuous improvement and innovation Workplace Options: This position is fully on-site, OR Hybrid/Flex with a minimum of three days on-site. While on-site, you will be a part of the SDA Team located at the Scottsdale, Arizona facility.
11/14/2026
Full time
Job Description Requisition #: 0 Employment Type: Contract Labor Job Title: CL - Space Force - Systems Network Engineer Location: US-AZ-Scottsdale Overview: Our client engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. With a global team of 12,000+ top professionals, we partner with the best in industry to expand the bounds of innovation in the defense and scientific arenas. Given the nature of our work and who we are, we value trust, honesty, alignment and transparency. We offer highly competitive benefits and pride ourselves in being a great place to work with a shared sense of purpose. You will also enjoy a flexible work environment where contributions are recognized and rewarded. If who we are and what we do resonates with you, we invite you to join our high-performance team! Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans Responsibilities: Bachelor's degree in Engineering, or a related Science or Mathematics field, plus a minimum of 8 years of relevant experience; or Master's degree plus a minimum of 6 years of relevant experience. CLEARANCE REQUIREMENTS: Department of Defense Secret security clearance is preferred at time of hire. Candidates must be able to obtain a Secret clearance within a reasonable amount of time from date of hire. Applicants selected may be subject to a U.S. Government security investigation and must meet eligibility requirements for access to classified information. Due to the nature of work performed within our facilities, U.S. citizenship is required. Qualifications: Our client is currently seeking a Systems Network Engineer in our Scottsdale, AZ facility. As a Space Force Systems Network Engineer, you'll design and plan network systems by providing specifications and detailed schematics for network architecture. You'll apply your extensive technical expertise to provide specific, detailed information for hardware and software selection, implementation techniques, and tools for the most efficient solution to meet business needs, including present and future capacity requirements. This is your chance to not just work with the best technologies, but actually help create them. Think you're up to the task? What you'll experience: Technologies that aren't just top-notch, they're often top-secret A team of bold thinkers committed to exploring what's next Opportunities to gain new knowledge - as it's discovered What you bring to the table: A Bachelor's degree in Engineering, a related specialized area or field is required (or equivalent experience) plus a minimum of 15 years of relevant experience in large networking systems Technical expertise in all areas of network and computer hardware and software interconnection and interfacing, such as routers, multiplexers, hubs, bridges and gateways, etc. Subject Matter Expert (SME) in at least one discipline/field with a working knowledge in multiple adjacent disciplines/fields Represent organization as prime technical contact on contracts and projects What sets you apart: Space and Ground Mission Experience Strong ability to develop solutions to highly complex problems that require significant ingenuity, innovation and creativity Space and Ground Mission Experience AWS Solutions Architect, Advanced Networking, Security Specialty, or equivalent cloud certification Cisco CCNP, Juniper JNCIP/JNCIE, Palo Alto, or comparable network/security certification.Experience with AWS SC2S, GovCloud, Direct Connect, Transit Gateway, and hybrid-cloud architectures Experience with Terraform, CloudFormation, Ansible, Python, or other automation/IaC tools Experience with SDA, PWSA, GMI, satellite ground systems, mission networks, or Space Vehicle Vendor integration.Experience with IPv6 design, implementation, and transition planning in enterprise or DoD environments Ability to lead a team and mentor staff Strong independent judgement Identifies opportunities to apply AI for continuous improvement and innovation Workplace Options: This position is fully on-site, OR Hybrid/Flex with a minimum of three days on-site. While on-site, you will be a part of the SDA Team located at the Scottsdale, Arizona facility.
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
11/13/2026
Full time
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 - Finance Tech Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Finance Tech is responsible for transforming Capital One's financial engine into the most trusted, nimble, and insightful in the industry. Our core mission is to engineer outcomes that strategically enable the Finance organization and the broader Enterprise to remain efficient. This mission is driven by four technical and functional pillars: Forecasting & Resiliency, providing advanced decision support; Treasury & Trade managing sophisticated cash and investment lifecycles; and Accounting & Reporting, ensuring the integrity of our general ledger and regulatory commitments. Powering these pillars are our Data and Core capabilities, which manage the orchestration, sourcing, and pipelines for critical financial data products. By defining the next generation of financial technology, we ensure that every business process is backed by high fidelity tracking and reporting. Together, we are not just supporting the bottom line - we are building a strategic enabler that operates as a world class financial engine. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
11/13/2026
Full time
Machine Learning Engineer 4 - Finance Tech Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Finance Tech is responsible for transforming Capital One's financial engine into the most trusted, nimble, and insightful in the industry. Our core mission is to engineer outcomes that strategically enable the Finance organization and the broader Enterprise to remain efficient. This mission is driven by four technical and functional pillars: Forecasting & Resiliency, providing advanced decision support; Treasury & Trade managing sophisticated cash and investment lifecycles; and Accounting & Reporting, ensuring the integrity of our general ledger and regulatory commitments. Powering these pillars are our Data and Core capabilities, which manage the orchestration, sourcing, and pipelines for critical financial data products. By defining the next generation of financial technology, we ensure that every business process is backed by high fidelity tracking and reporting. Together, we are not just supporting the bottom line - we are building a strategic enabler that operates as a world class financial engine. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 - Finance Tech Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Finance Tech is responsible for transforming Capital One's financial engine into the most trusted, nimble, and insightful in the industry. Our core mission is to engineer outcomes that strategically enable the Finance organization and the broader Enterprise to remain efficient. This mission is driven by four technical and functional pillars: Forecasting & Resiliency, providing advanced decision support; Treasury & Trade managing sophisticated cash and investment lifecycles; and Accounting & Reporting, ensuring the integrity of our general ledger and regulatory commitments. Powering these pillars are our Data and Core capabilities, which manage the orchestration, sourcing, and pipelines for critical financial data products. By defining the next generation of financial technology, we ensure that every business process is backed by high fidelity tracking and reporting. Together, we are not just supporting the bottom line - we are building a strategic enabler that operates as a world class financial engine. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
11/13/2026
Full time
Machine Learning Engineer 4 - Finance Tech Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Finance Tech is responsible for transforming Capital One's financial engine into the most trusted, nimble, and insightful in the industry. Our core mission is to engineer outcomes that strategically enable the Finance organization and the broader Enterprise to remain efficient. This mission is driven by four technical and functional pillars: Forecasting & Resiliency, providing advanced decision support; Treasury & Trade managing sophisticated cash and investment lifecycles; and Accounting & Reporting, ensuring the integrity of our general ledger and regulatory commitments. Powering these pillars are our Data and Core capabilities, which manage the orchestration, sourcing, and pipelines for critical financial data products. By defining the next generation of financial technology, we ensure that every business process is backed by high fidelity tracking and reporting. Together, we are not just supporting the bottom line - we are building a strategic enabler that operates as a world class financial engine. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 - Manager IC Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. As part of the Enterprise ML Platform team, you will build and evolve the high-throughput, multi-tenant training platform that powers model workflows across Capital One-ranging from classical ML to GenAI fine-tuning and distributed computing with Ray, Spark, and Dask. Owning the Kubeflow Pipelines v2 application layer on Amazon EKS, the team develops internal Python and Go SDKs, custom Kubernetes operators, and developer tooling that abstract complex infrastructure for critical, regulated workloads. In this role, you will integrate agentic AI into modern engineering lifecycles and contribute directly to upstream open-source Kubeflow, balancing fast-paced feature delivery with enterprise-grade platform stability and security. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years designing, training, optimizing, and evaluating modern ML models across architectures (e.g., Transformers, CNNs, RNNs) and paradigms (supervised, unsupervised, reinforcement learning), including loss design, hyperparameter tuning, and diagnostic debugging. 3+ years architecting and scaling distributed, end-to-end ML systems, from production-ready training/evaluation data pipelines to algorithm and infrastructure optimization. 3+ years managing ML workloads using Docker, Kubernetes, and pipeline orchestration tools (e.g., Kubeflow is a strong plus). 3+ years applying modern software best practices (CI/CD, version control, automated testing, code reviews) and deploying resilient systems with progressive rollout strategies (blue/green, canary/one-box), monitoring, alerting, and incident response planning. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
11/13/2026
Full time
Machine Learning Engineer 4 - Manager IC Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. As part of the Enterprise ML Platform team, you will build and evolve the high-throughput, multi-tenant training platform that powers model workflows across Capital One-ranging from classical ML to GenAI fine-tuning and distributed computing with Ray, Spark, and Dask. Owning the Kubeflow Pipelines v2 application layer on Amazon EKS, the team develops internal Python and Go SDKs, custom Kubernetes operators, and developer tooling that abstract complex infrastructure for critical, regulated workloads. In this role, you will integrate agentic AI into modern engineering lifecycles and contribute directly to upstream open-source Kubeflow, balancing fast-paced feature delivery with enterprise-grade platform stability and security. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years designing, training, optimizing, and evaluating modern ML models across architectures (e.g., Transformers, CNNs, RNNs) and paradigms (supervised, unsupervised, reinforcement learning), including loss design, hyperparameter tuning, and diagnostic debugging. 3+ years architecting and scaling distributed, end-to-end ML systems, from production-ready training/evaluation data pipelines to algorithm and infrastructure optimization. 3+ years managing ML workloads using Docker, Kubernetes, and pipeline orchestration tools (e.g., Kubeflow is a strong plus). 3+ years applying modern software best practices (CI/CD, version control, automated testing, code reviews) and deploying resilient systems with progressive rollout strategies (blue/green, canary/one-box), monitoring, alerting, and incident response planning. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 - Manager IC Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. As part of the Enterprise ML Platform team, you will build and evolve the high-throughput, multi-tenant training platform that powers model workflows across Capital One-ranging from classical ML to GenAI fine-tuning and distributed computing with Ray, Spark, and Dask. Owning the Kubeflow Pipelines v2 application layer on Amazon EKS, the team develops internal Python and Go SDKs, custom Kubernetes operators, and developer tooling that abstract complex infrastructure for critical, regulated workloads. In this role, you will integrate agentic AI into modern engineering lifecycles and contribute directly to upstream open-source Kubeflow, balancing fast-paced feature delivery with enterprise-grade platform stability and security. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years designing, training, optimizing, and evaluating modern ML models across architectures (e.g., Transformers, CNNs, RNNs) and paradigms (supervised, unsupervised, reinforcement learning), including loss design, hyperparameter tuning, and diagnostic debugging. 3+ years architecting and scaling distributed, end-to-end ML systems, from production-ready training/evaluation data pipelines to algorithm and infrastructure optimization. 3+ years managing ML workloads using Docker, Kubernetes, and pipeline orchestration tools (e.g., Kubeflow is a strong plus). 3+ years applying modern software best practices (CI/CD, version control, automated testing, code reviews) and deploying resilient systems with progressive rollout strategies (blue/green, canary/one-box), monitoring, alerting, and incident response planning. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
11/13/2026
Full time
Machine Learning Engineer 4 - Manager IC Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. As part of the Enterprise ML Platform team, you will build and evolve the high-throughput, multi-tenant training platform that powers model workflows across Capital One-ranging from classical ML to GenAI fine-tuning and distributed computing with Ray, Spark, and Dask. Owning the Kubeflow Pipelines v2 application layer on Amazon EKS, the team develops internal Python and Go SDKs, custom Kubernetes operators, and developer tooling that abstract complex infrastructure for critical, regulated workloads. In this role, you will integrate agentic AI into modern engineering lifecycles and contribute directly to upstream open-source Kubeflow, balancing fast-paced feature delivery with enterprise-grade platform stability and security. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years designing, training, optimizing, and evaluating modern ML models across architectures (e.g., Transformers, CNNs, RNNs) and paradigms (supervised, unsupervised, reinforcement learning), including loss design, hyperparameter tuning, and diagnostic debugging. 3+ years architecting and scaling distributed, end-to-end ML systems, from production-ready training/evaluation data pipelines to algorithm and infrastructure optimization. 3+ years managing ML workloads using Docker, Kubernetes, and pipeline orchestration tools (e.g., Kubeflow is a strong plus). 3+ years applying modern software best practices (CI/CD, version control, automated testing, code reviews) and deploying resilient systems with progressive rollout strategies (blue/green, canary/one-box), monitoring, alerting, and incident response planning. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
11/13/2026
Full time
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Job Description Job Description Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. We approach every challenge with ambition and commitment, holding ourselves and each other accountable to achieve the best results. When you join Appian, you'll be part of a passionate team dedicated to accomplishing hard things, together. Principal ML Platform Engineer Lead the engineering of software that matters - driving AI automation for the world's largest enterprises. This role is based at our h eadquarters in McLean, Virginia. Appian was built on a culture of in-person collaboration, which we believe is a key driver of our mission to be the best. Employees hired for this position are expected to be in the office 5 days a week to foster that culture and ensure we continue to thrive through shared ideas and teamwork. We believe being in the office provides more opportunities to come together and celebrate working with the exceptional people across Appian. About the Team Appian Engineering spans the full depth of our platform: from the foundational layers that power enterprise scale, to the AI capabilities redefining what automation can do. We operate in a highly collaborative, fast-paced environment focused on technical precision, continuous learning, and high code quality. By joining our team, you will solve real-world problems that directly shape how Appian delivers our AI-Powered Process Automation platform to enterprises around the world. The Opportunity As a Principal Software Engineer, you will serve as a technical linchpin for the team, bringing deep expertise in cloud-native architecture and a track record of influencing engineering direction beyond your immediate scope. Equipped with cutting-edge AI tooling, you will drive the design and delivery of high-complexity engineering solutions, set the technical bar for the team, and lead other engineers towards solutions of real complexity to ensure flawless Enterprise-Grade Orchestration. What You'll Do Develop Clean Software: Architect, build, and optimize high-performance software systems while maintaining a strong personal technical presence on the team. Lead Platform Modernization: Spearhead strategic technological changes and champion code refactoring efforts to keep the core Appian codebase cutting-edge, modern, and performant. Engineer with AI: Use AI coding tools fluently as a force multiplier: generating, reviewing, and critically evaluating AI-assisted code to ship faster without compromising quality or correctness. Lead Architecture & Delivery: Drive technical story breakdowns, acceptance criteria, and architectural design across complex, multi-tier application layers - from feature scoping through implementation. Optimize Performance & Scale: Manage product availability, latency, scalability, and efficiency by engineering deep reliability into our core software systems and performing advanced system tuning. Drive Engineering Excellence: Radiate development best practices across the department, perform meticulous code reviews on design and implementation, and build automation frameworks to prevent problem recurrence. Lead & Grow Engineers: Actively coach and mentor engineers at multiple levels, identify and close skill gaps on the team, and take ownership of accelerating the technical growth of those around you. Influence Technical Documentation: Share your expert domain knowledge regularly across the department, building a reputation as a vital resource and publishing high-quality content to Engineering's permanent documentation site. Required Qualifications Education: Minimum of a Bachelor of Science degree in Computer Science or a related technical/analytical discipline. (Equivalent experience is not accepted in lieu of a degree). Experience: 10+ years of relevant software development experience with a BS (or 8+ years of experience paired with a Master of Science in Computer Science or related field). Technical Mastery: Expert coding, scripting, and debugging proficiency in one or more core enterprise programming languages, specifically Java, Python, or Go. Domain Expertise: Deep working knowledge of distributed systems, cloud infrastructure, and the ability to contribute meaningfully at a senior individual contributor level within that space. Cross-Team Influence: Demonstrated ability to drive technical decisions and shape engineering practices beyond a single team or project scope. AI-Augmented Development: Demonstrated experience using AI coding assistants and a strong ability to evaluate, coach others on, and selectively apply AI-generated code in a production engineering context. Production Mastery: Proven experience developing, optimizing, and maintaining a high-volume, mission-critical production service environment. Communication & Alignment: Exceptional ability to communicate highly technical architectures verbally, visually, and in writing to diverse engineering audiences. Preferred Qualifications Cloud Architecture: Strong experience designing microservices, working with containerization (Docker, Kubernetes), and implementing modern CI/CD pipelines. Cloud Platforms: Deep experience developing and operating infrastructure across public cloud ecosystems, specifically AWS, Azure, and/or GCP. We value experience with enterprise platforms such as Salesforce or ServiceNow, as these skills translate well into our Enterprise-Grade Orchestration environment. What We Equip You With High-Impact Autonomy: A leadership environment where you will have real ownership over your team's direction, the latitude to make meaningful decisions, and participation in broader Engineering discussions. New Hire Orientation: A robust onboarding experience designed to integrate you smoothly into our technology, culture, and leadership model so you can show up for your team from day one. Continuous Enablement: Access to premier learning resources and dedicated learning time focused on both your continued technical growth and your evolution as an engineering leader. Sponsored Certifications: Full corporate sponsorship for professional technical certifications to advance your engineering credentials. The base salary range represents a good faith and reasonable estimate of the range at the time of posting. Actual compensation will be dependent on a number of factors including, but not limited to, the candidate's relevant work experience, qualifications, internal peer equity, and market and business conditions that exist when extending an offer. A discretionary bonus may be awarded in recognition of individual and company performance. In addition, Appian provides generous benefits offerings that include a 401(k) plan with company match, flexible time off, paid parental leave, medical, dental, and vision plans, life insurance, disability insurance, wellness programs, flexible spending accounts, health savings account contributions, an employee referral bonus program, and learning and development resources. Certain positions may be eligible for equity awards. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation, commission, bonus, or benefit plans. Base Salary Range $175,000-$325,000 USD Tools and Resources Training and Development: During onboarding, we focus on equipping new hires with the skills and knowledge for success through department-specific training. Continuous learning is a central focus at Appian, with dedicated mentorship and the First-Friend program being widely utilized resources for new hires. Growth Opportunities: Appian provides a diverse array of growth and development opportunities, including our leadership program tailored for new and aspiring managers, a comprehensive library of specialized department training through Appian University, skills based training, and tuition reimbursement for those aiming to advance their education. This commitment ensures that employees have access to a holistic range of development opportunities. Community: We'll immerse you into our community rooted in respect starting on day one. Appian fosters inclusivity through our 8 employee-led affinity groups. These groups help employees build stronger internal and external networks by planning social, educational, and outreach activities to connect with Appianites and larger initiatives throughout the company. Benefits Appian offers a comprehensive benefits package designed to support your health, wellbeing, and financial future. Benefits may include health coverage, Employee Assistance Program (EAP) with free mental health support, life and disability insurance, an Employee Stock Purchase Program (ESPP), a retirement/pension plan, wellness dollars, tuition reimbursement, family-forming benefits and more. Benefits vary by country-please ask your Talent Acquisition contact for details specific to the location you are applying to. About Appian Appian provides AI automation for mission-critical work. We automate complex processes in large enterprises and governments . click apply for full job details
10/07/2026
Full time
Job Description Job Description Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. We approach every challenge with ambition and commitment, holding ourselves and each other accountable to achieve the best results. When you join Appian, you'll be part of a passionate team dedicated to accomplishing hard things, together. Principal ML Platform Engineer Lead the engineering of software that matters - driving AI automation for the world's largest enterprises. This role is based at our h eadquarters in McLean, Virginia. Appian was built on a culture of in-person collaboration, which we believe is a key driver of our mission to be the best. Employees hired for this position are expected to be in the office 5 days a week to foster that culture and ensure we continue to thrive through shared ideas and teamwork. We believe being in the office provides more opportunities to come together and celebrate working with the exceptional people across Appian. About the Team Appian Engineering spans the full depth of our platform: from the foundational layers that power enterprise scale, to the AI capabilities redefining what automation can do. We operate in a highly collaborative, fast-paced environment focused on technical precision, continuous learning, and high code quality. By joining our team, you will solve real-world problems that directly shape how Appian delivers our AI-Powered Process Automation platform to enterprises around the world. The Opportunity As a Principal Software Engineer, you will serve as a technical linchpin for the team, bringing deep expertise in cloud-native architecture and a track record of influencing engineering direction beyond your immediate scope. Equipped with cutting-edge AI tooling, you will drive the design and delivery of high-complexity engineering solutions, set the technical bar for the team, and lead other engineers towards solutions of real complexity to ensure flawless Enterprise-Grade Orchestration. What You'll Do Develop Clean Software: Architect, build, and optimize high-performance software systems while maintaining a strong personal technical presence on the team. Lead Platform Modernization: Spearhead strategic technological changes and champion code refactoring efforts to keep the core Appian codebase cutting-edge, modern, and performant. Engineer with AI: Use AI coding tools fluently as a force multiplier: generating, reviewing, and critically evaluating AI-assisted code to ship faster without compromising quality or correctness. Lead Architecture & Delivery: Drive technical story breakdowns, acceptance criteria, and architectural design across complex, multi-tier application layers - from feature scoping through implementation. Optimize Performance & Scale: Manage product availability, latency, scalability, and efficiency by engineering deep reliability into our core software systems and performing advanced system tuning. Drive Engineering Excellence: Radiate development best practices across the department, perform meticulous code reviews on design and implementation, and build automation frameworks to prevent problem recurrence. Lead & Grow Engineers: Actively coach and mentor engineers at multiple levels, identify and close skill gaps on the team, and take ownership of accelerating the technical growth of those around you. Influence Technical Documentation: Share your expert domain knowledge regularly across the department, building a reputation as a vital resource and publishing high-quality content to Engineering's permanent documentation site. Required Qualifications Education: Minimum of a Bachelor of Science degree in Computer Science or a related technical/analytical discipline. (Equivalent experience is not accepted in lieu of a degree). Experience: 10+ years of relevant software development experience with a BS (or 8+ years of experience paired with a Master of Science in Computer Science or related field). Technical Mastery: Expert coding, scripting, and debugging proficiency in one or more core enterprise programming languages, specifically Java, Python, or Go. Domain Expertise: Deep working knowledge of distributed systems, cloud infrastructure, and the ability to contribute meaningfully at a senior individual contributor level within that space. Cross-Team Influence: Demonstrated ability to drive technical decisions and shape engineering practices beyond a single team or project scope. AI-Augmented Development: Demonstrated experience using AI coding assistants and a strong ability to evaluate, coach others on, and selectively apply AI-generated code in a production engineering context. Production Mastery: Proven experience developing, optimizing, and maintaining a high-volume, mission-critical production service environment. Communication & Alignment: Exceptional ability to communicate highly technical architectures verbally, visually, and in writing to diverse engineering audiences. Preferred Qualifications Cloud Architecture: Strong experience designing microservices, working with containerization (Docker, Kubernetes), and implementing modern CI/CD pipelines. Cloud Platforms: Deep experience developing and operating infrastructure across public cloud ecosystems, specifically AWS, Azure, and/or GCP. We value experience with enterprise platforms such as Salesforce or ServiceNow, as these skills translate well into our Enterprise-Grade Orchestration environment. What We Equip You With High-Impact Autonomy: A leadership environment where you will have real ownership over your team's direction, the latitude to make meaningful decisions, and participation in broader Engineering discussions. New Hire Orientation: A robust onboarding experience designed to integrate you smoothly into our technology, culture, and leadership model so you can show up for your team from day one. Continuous Enablement: Access to premier learning resources and dedicated learning time focused on both your continued technical growth and your evolution as an engineering leader. Sponsored Certifications: Full corporate sponsorship for professional technical certifications to advance your engineering credentials. The base salary range represents a good faith and reasonable estimate of the range at the time of posting. Actual compensation will be dependent on a number of factors including, but not limited to, the candidate's relevant work experience, qualifications, internal peer equity, and market and business conditions that exist when extending an offer. A discretionary bonus may be awarded in recognition of individual and company performance. In addition, Appian provides generous benefits offerings that include a 401(k) plan with company match, flexible time off, paid parental leave, medical, dental, and vision plans, life insurance, disability insurance, wellness programs, flexible spending accounts, health savings account contributions, an employee referral bonus program, and learning and development resources. Certain positions may be eligible for equity awards. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation, commission, bonus, or benefit plans. Base Salary Range $175,000-$325,000 USD Tools and Resources Training and Development: During onboarding, we focus on equipping new hires with the skills and knowledge for success through department-specific training. Continuous learning is a central focus at Appian, with dedicated mentorship and the First-Friend program being widely utilized resources for new hires. Growth Opportunities: Appian provides a diverse array of growth and development opportunities, including our leadership program tailored for new and aspiring managers, a comprehensive library of specialized department training through Appian University, skills based training, and tuition reimbursement for those aiming to advance their education. This commitment ensures that employees have access to a holistic range of development opportunities. Community: We'll immerse you into our community rooted in respect starting on day one. Appian fosters inclusivity through our 8 employee-led affinity groups. These groups help employees build stronger internal and external networks by planning social, educational, and outreach activities to connect with Appianites and larger initiatives throughout the company. Benefits Appian offers a comprehensive benefits package designed to support your health, wellbeing, and financial future. Benefits may include health coverage, Employee Assistance Program (EAP) with free mental health support, life and disability insurance, an Employee Stock Purchase Program (ESPP), a retirement/pension plan, wellness dollars, tuition reimbursement, family-forming benefits and more. Benefits vary by country-please ask your Talent Acquisition contact for details specific to the location you are applying to. About Appian Appian provides AI automation for mission-critical work. We automate complex processes in large enterprises and governments . click apply for full job details
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
10/02/2026
Full time
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
10/02/2026
Full time
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
10/02/2026
Full time
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
10/02/2026
Full time
Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Job Description Job Description Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. We approach every challenge with ambition and commitment, holding ourselves and each other accountable to achieve the best results. When you join Appian, you'll be part of a passionate team dedicated to accomplishing hard things, together. Principal ML Platform Engineer Location: Remote (US) Lead the engineering of software that matters - driving AI automation for the world's largest enterprises. About the Team Appian Engineering spans the full depth of our platform: from the foundational layers that power enterprise scale, to the AI capabilities redefining what automation can do. We operate in a highly collaborative, fast-paced environment focused on technical precision, continuous learning, and high code quality. By joining our team, you will solve real-world problems that directly shape how Appian delivers our AI-Powered Process Automation platform to enterprises around the world. The Opportunity As a Principal Software Engineer, you will serve as a technical linchpin for the team, bringing deep expertise in cloud-native architecture and a track record of influencing engineering direction beyond your immediate scope. Equipped with cutting-edge AI tooling, you will drive the design and delivery of high-complexity engineering solutions, set the technical bar for the team, and lead other engineers towards solutions of real complexity to ensure flawless Enterprise-Grade Orchestration. What You'll Do Develop Clean Software: Architect, build, and optimize high-performance software systems while maintaining a strong personal technical presence on the team. Lead Platform Modernization: Spearhead strategic technological changes and champion code refactoring efforts to keep the core Appian codebase cutting-edge, modern, and performant. Engineer with AI: Use AI coding tools fluently as a force multiplier: generating, reviewing, and critically evaluating AI-assisted code to ship faster without compromising quality or correctness. Lead Architecture & Delivery: Drive technical story breakdowns, acceptance criteria, and architectural design across complex, multi-tier application layers - from feature scoping through implementation. Optimize Performance & Scale: Manage product availability, latency, scalability, and efficiency by engineering deep reliability into our core software systems and performing advanced system tuning. Drive Engineering Excellence: Radiate development best practices across the department, perform meticulous code reviews on design and implementation, and build automation frameworks to prevent problem recurrence. Lead & Grow Engineers: Actively coach and mentor engineers at multiple levels, identify and close skill gaps on the team, and take ownership of accelerating the technical growth of those around you. Influence Technical Documentation: Share your expert domain knowledge regularly across the department, building a reputation as a vital resource and publishing high-quality content to Engineering's permanent documentation site. Required Qualifications Education: Minimum of a Bachelor of Science degree in Computer Science or a related technical/analytical discipline. (Equivalent experience is not accepted in lieu of a degree). Experience: 10+ years of relevant software development experience with a BS (or 8+ years of experience paired with a Master of Science in Computer Science or related field). Technical Mastery: Expert coding, scripting, and debugging proficiency in one or more core enterprise programming languages, specifically Java, Python, or Go. Domain Expertise: Deep working knowledge of distributed systems, cloud infrastructure, and the ability to contribute meaningfully at a senior individual contributor level within that space. Cross-Team Influence: Demonstrated ability to drive technical decisions and shape engineering practices beyond a single team or project scope. AI-Augmented Development: Demonstrated experience using AI coding assistants and a strong ability to evaluate, coach others on, and selectively apply AI-generated code in a production engineering context. Production Mastery: Proven experience developing, optimizing, and maintaining a high-volume, mission-critical production service environment. Communication & Alignment: Exceptional ability to communicate highly technical architectures verbally, visually, and in writing to diverse engineering audiences. Preferred Qualifications Cloud Architecture: Strong experience designing microservices, working with containerization (Docker, Kubernetes), and implementing modern CI/CD pipelines. Cloud Platforms: Deep experience developing and operating infrastructure across public cloud ecosystems, specifically AWS, Azure, and/or GCP. We value experience with enterprise platforms such as Salesforce or ServiceNow, as these skills translate well into our Enterprise-Grade Orchestration environment. What We Equip You With High-Impact Autonomy: A leadership environment where you will have real ownership over your team's direction, the latitude to make meaningful decisions, and participation in broader Engineering discussions. New Hire Orientation: A robust onboarding experience designed to integrate you smoothly into our technology, culture, and leadership model so you can show up for your team from day one. Continuous Enablement: Access to premier learning resources and dedicated learning time focused on both your continued technical growth and your evolution as an engineering leader. Sponsored Certifications: Full corporate sponsorship for professional technical certifications to advance your engineering credentials. The base salary range represents a good faith and reasonable estimate of the range at the time of posting. Actual compensation will be dependent on a number of factors including, but not limited to, the candidate's relevant work experience, qualifications, internal peer equity, and market and business conditions that exist when extending an offer. A discretionary bonus may be awarded in recognition of individual and company performance. In addition, Appian provides generous benefits offerings that include a 401(k) plan with company match, flexible time off, paid parental leave, medical, dental, and vision plans, life insurance, disability insurance, wellness programs, flexible spending accounts, health savings account contributions, an employee referral bonus program, and learning and development resources. Certain positions may be eligible for equity awards. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation, commission, bonus, or benefit plans. Base Salary Range $175,000-$325,000 USD Tools and Resources Training and Development: During onboarding, we focus on equipping new hires with the skills and knowledge for success through department-specific training. Continuous learning is a central focus at Appian, with dedicated mentorship and the First-Friend program being widely utilized resources for new hires. Growth Opportunities: Appian provides a diverse array of growth and development opportunities, including our leadership program tailored for new and aspiring managers, a comprehensive library of specialized department training through Appian University, skills based training, and tuition reimbursement for those aiming to advance their education. This commitment ensures that employees have access to a holistic range of development opportunities. Community: We'll immerse you into our community rooted in respect starting on day one. Appian fosters inclusivity through our 8 employee-led affinity groups. These groups help employees build stronger internal and external networks by planning social, educational, and outreach activities to connect with Appianites and larger initiatives throughout the company. Benefits Appian offers a comprehensive benefits package designed to support your health, wellbeing, and financial future. Benefits may include health coverage, Employee Assistance Program (EAP) with free mental health support, life and disability insurance, an Employee Stock Purchase Program (ESPP), a retirement/pension plan, wellness dollars, tuition reimbursement, family-forming benefits and more. Benefits vary by country-please ask your Talent Acquisition contact for details specific to the location you are applying to. About Appian Appian provides AI automation for mission-critical work. We automate complex processes in large enterprises and governments. Our platform is known for its unique reliability and scale. We've been automating processes for more than 25 years and understand enterprise operations like no one else. For more information, visit Nasdaq: APPN Follow Appian: LinkedIn, Youtube, Instagram, Facebook Appian is an equal opportunity employer that strives to attract and retain the best talent . click apply for full job details
09/30/2026
Full time
Job Description Job Description Here at Appian, our values of Intensity and Excellence define who we are. We set high standards and live up to them, ensuring that everything we do is done with care and quality. We approach every challenge with ambition and commitment, holding ourselves and each other accountable to achieve the best results. When you join Appian, you'll be part of a passionate team dedicated to accomplishing hard things, together. Principal ML Platform Engineer Location: Remote (US) Lead the engineering of software that matters - driving AI automation for the world's largest enterprises. About the Team Appian Engineering spans the full depth of our platform: from the foundational layers that power enterprise scale, to the AI capabilities redefining what automation can do. We operate in a highly collaborative, fast-paced environment focused on technical precision, continuous learning, and high code quality. By joining our team, you will solve real-world problems that directly shape how Appian delivers our AI-Powered Process Automation platform to enterprises around the world. The Opportunity As a Principal Software Engineer, you will serve as a technical linchpin for the team, bringing deep expertise in cloud-native architecture and a track record of influencing engineering direction beyond your immediate scope. Equipped with cutting-edge AI tooling, you will drive the design and delivery of high-complexity engineering solutions, set the technical bar for the team, and lead other engineers towards solutions of real complexity to ensure flawless Enterprise-Grade Orchestration. What You'll Do Develop Clean Software: Architect, build, and optimize high-performance software systems while maintaining a strong personal technical presence on the team. Lead Platform Modernization: Spearhead strategic technological changes and champion code refactoring efforts to keep the core Appian codebase cutting-edge, modern, and performant. Engineer with AI: Use AI coding tools fluently as a force multiplier: generating, reviewing, and critically evaluating AI-assisted code to ship faster without compromising quality or correctness. Lead Architecture & Delivery: Drive technical story breakdowns, acceptance criteria, and architectural design across complex, multi-tier application layers - from feature scoping through implementation. Optimize Performance & Scale: Manage product availability, latency, scalability, and efficiency by engineering deep reliability into our core software systems and performing advanced system tuning. Drive Engineering Excellence: Radiate development best practices across the department, perform meticulous code reviews on design and implementation, and build automation frameworks to prevent problem recurrence. Lead & Grow Engineers: Actively coach and mentor engineers at multiple levels, identify and close skill gaps on the team, and take ownership of accelerating the technical growth of those around you. Influence Technical Documentation: Share your expert domain knowledge regularly across the department, building a reputation as a vital resource and publishing high-quality content to Engineering's permanent documentation site. Required Qualifications Education: Minimum of a Bachelor of Science degree in Computer Science or a related technical/analytical discipline. (Equivalent experience is not accepted in lieu of a degree). Experience: 10+ years of relevant software development experience with a BS (or 8+ years of experience paired with a Master of Science in Computer Science or related field). Technical Mastery: Expert coding, scripting, and debugging proficiency in one or more core enterprise programming languages, specifically Java, Python, or Go. Domain Expertise: Deep working knowledge of distributed systems, cloud infrastructure, and the ability to contribute meaningfully at a senior individual contributor level within that space. Cross-Team Influence: Demonstrated ability to drive technical decisions and shape engineering practices beyond a single team or project scope. AI-Augmented Development: Demonstrated experience using AI coding assistants and a strong ability to evaluate, coach others on, and selectively apply AI-generated code in a production engineering context. Production Mastery: Proven experience developing, optimizing, and maintaining a high-volume, mission-critical production service environment. Communication & Alignment: Exceptional ability to communicate highly technical architectures verbally, visually, and in writing to diverse engineering audiences. Preferred Qualifications Cloud Architecture: Strong experience designing microservices, working with containerization (Docker, Kubernetes), and implementing modern CI/CD pipelines. Cloud Platforms: Deep experience developing and operating infrastructure across public cloud ecosystems, specifically AWS, Azure, and/or GCP. We value experience with enterprise platforms such as Salesforce or ServiceNow, as these skills translate well into our Enterprise-Grade Orchestration environment. What We Equip You With High-Impact Autonomy: A leadership environment where you will have real ownership over your team's direction, the latitude to make meaningful decisions, and participation in broader Engineering discussions. New Hire Orientation: A robust onboarding experience designed to integrate you smoothly into our technology, culture, and leadership model so you can show up for your team from day one. Continuous Enablement: Access to premier learning resources and dedicated learning time focused on both your continued technical growth and your evolution as an engineering leader. Sponsored Certifications: Full corporate sponsorship for professional technical certifications to advance your engineering credentials. The base salary range represents a good faith and reasonable estimate of the range at the time of posting. Actual compensation will be dependent on a number of factors including, but not limited to, the candidate's relevant work experience, qualifications, internal peer equity, and market and business conditions that exist when extending an offer. A discretionary bonus may be awarded in recognition of individual and company performance. In addition, Appian provides generous benefits offerings that include a 401(k) plan with company match, flexible time off, paid parental leave, medical, dental, and vision plans, life insurance, disability insurance, wellness programs, flexible spending accounts, health savings account contributions, an employee referral bonus program, and learning and development resources. Certain positions may be eligible for equity awards. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation, commission, bonus, or benefit plans. Base Salary Range $175,000-$325,000 USD Tools and Resources Training and Development: During onboarding, we focus on equipping new hires with the skills and knowledge for success through department-specific training. Continuous learning is a central focus at Appian, with dedicated mentorship and the First-Friend program being widely utilized resources for new hires. Growth Opportunities: Appian provides a diverse array of growth and development opportunities, including our leadership program tailored for new and aspiring managers, a comprehensive library of specialized department training through Appian University, skills based training, and tuition reimbursement for those aiming to advance their education. This commitment ensures that employees have access to a holistic range of development opportunities. Community: We'll immerse you into our community rooted in respect starting on day one. Appian fosters inclusivity through our 8 employee-led affinity groups. These groups help employees build stronger internal and external networks by planning social, educational, and outreach activities to connect with Appianites and larger initiatives throughout the company. Benefits Appian offers a comprehensive benefits package designed to support your health, wellbeing, and financial future. Benefits may include health coverage, Employee Assistance Program (EAP) with free mental health support, life and disability insurance, an Employee Stock Purchase Program (ESPP), a retirement/pension plan, wellness dollars, tuition reimbursement, family-forming benefits and more. Benefits vary by country-please ask your Talent Acquisition contact for details specific to the location you are applying to. About Appian Appian provides AI automation for mission-critical work. We automate complex processes in large enterprises and governments. Our platform is known for its unique reliability and scale. We've been automating processes for more than 25 years and understand enterprise operations like no one else. For more information, visit Nasdaq: APPN Follow Appian: LinkedIn, Youtube, Instagram, Facebook Appian is an equal opportunity employer that strives to attract and retain the best talent . click apply for full job details
Job Description Job Description Job Title: Microsoft Endpoint & Workspace ONE Specialist Duration: Full-Time Hire / Permanent Role Location: Reston, VA or Plano, TX. Work Mode: Hybrid Duties and Responsibilities Systemone is seeking a Microsoft Endpoint & Workspace ONE Specialist? to support a large-scale enterprise environment within a cloud first infrastructure. This role is responsible for managing and supporting endpoint devices, mobile platforms, and enterprise mobility solutions while ensuring compliance, security, and operational stability across the environment. The ideal candidate will have experience supporting Apple macOS devices, mobile device management (MDM) platforms such as Omnissa Workspace ONE and Microsoft Intune, and enterprise mobile technologies. Responsibilities include troubleshooting endpoint and mobile device issues, monitoring system performance and compliance, analyzing logs to identify and resolve incidents, and partnering with engineering, security, and compliance teams to maintain endpoint protection standards. The role also requires strong documentation practices, incident management capabilities, and the ability to support change management initiatives in a regulated financial services environment. Required Qualifications . Hands on experience with Omnissa Workspace ONE and/or Microsoft Intune Experience supporting and troubleshooting Apple macOS environments Mobile device management and support for iOS and Android devices Ability to monitor system performance, compliance status, and operational health Experience analyzing logs and collaborating with engineering teams to resolve incidents Understanding of endpoint security controls, policy enforcement, and compliance requirements Strong troubleshooting and root cause analysis skills Experience documenting incidents, resolutions, and operational procedures with attention to detail Familiarity with IT Service Management (ITSM) processes and change management practices Experience working in enterprise cloud environments, preferably AWS based organizations Knowledge of ServiceNow for incident, problem, and change management processes Strong written and verbal communication skills Required Skills Amazon Web Services Cloud Analytical Thinking Communication Detail-oriented Problem Solving ServiceNow Change Management Desired Skillset: ? Relevant certifications such as Microsoft Intune, Workspace ONE, AWS Cloud Practitioner, or ITIL are a plus Education: Bachelor's degree in computer science, Information Systems, or a related field.? Ref: Pittsburgh
09/30/2026
Full time
Job Description Job Description Job Title: Microsoft Endpoint & Workspace ONE Specialist Duration: Full-Time Hire / Permanent Role Location: Reston, VA or Plano, TX. Work Mode: Hybrid Duties and Responsibilities Systemone is seeking a Microsoft Endpoint & Workspace ONE Specialist? to support a large-scale enterprise environment within a cloud first infrastructure. This role is responsible for managing and supporting endpoint devices, mobile platforms, and enterprise mobility solutions while ensuring compliance, security, and operational stability across the environment. The ideal candidate will have experience supporting Apple macOS devices, mobile device management (MDM) platforms such as Omnissa Workspace ONE and Microsoft Intune, and enterprise mobile technologies. Responsibilities include troubleshooting endpoint and mobile device issues, monitoring system performance and compliance, analyzing logs to identify and resolve incidents, and partnering with engineering, security, and compliance teams to maintain endpoint protection standards. The role also requires strong documentation practices, incident management capabilities, and the ability to support change management initiatives in a regulated financial services environment. Required Qualifications . Hands on experience with Omnissa Workspace ONE and/or Microsoft Intune Experience supporting and troubleshooting Apple macOS environments Mobile device management and support for iOS and Android devices Ability to monitor system performance, compliance status, and operational health Experience analyzing logs and collaborating with engineering teams to resolve incidents Understanding of endpoint security controls, policy enforcement, and compliance requirements Strong troubleshooting and root cause analysis skills Experience documenting incidents, resolutions, and operational procedures with attention to detail Familiarity with IT Service Management (ITSM) processes and change management practices Experience working in enterprise cloud environments, preferably AWS based organizations Knowledge of ServiceNow for incident, problem, and change management processes Strong written and verbal communication skills Required Skills Amazon Web Services Cloud Analytical Thinking Communication Detail-oriented Problem Solving ServiceNow Change Management Desired Skillset: ? Relevant certifications such as Microsoft Intune, Workspace ONE, AWS Cloud Practitioner, or ITIL are a plus Education: Bachelor's degree in computer science, Information Systems, or a related field.? Ref: Pittsburgh
