Description: We are a family-oriented Midwest based company in Wisconsin, Minnesota, and Michigan. As a result of our growth, we have a need for an Application Engineer to utilize sales and technical capabilities to grow Crane business within an assigned market. Our mission is to help customers succeed. To achieve our mission, we've instilled a culture and environment that encourages new ideas, promotes experimentation, and inspires innovation. Crane Engineering team members impact the organization's success and are recognized for creating "WOW!" customer experiences. What You Get To Do Develops and maintains the product and application knowledge necessary to service internal and external customers within the assigned market. Supports and keeps sales and management staff informed of major projects; assists office and warehouse staff with application and product information. Provides customer and employee product training and sales call technical support. Provides technical sales and marketing support to Crane customers and staff. Provides the highest quality quotations possible for the specific application as outlined by the customer or Account Manager. Accurately prices, verifies contractual obligations, internal costs, agency sales and stock/drop-shipments. Develops the product and application knowledge necessary to service the needs of our customers and sales staff May perform other duties as assigned. Requirements: What We Need From You Bachelor's degree in a related engineering field preferred. Minimum of one-year related experience and/or training; or equivalent combination of education and experience. An engineering professional with appropriate industry/educational experience. A customer-service focused individual that successfully responds to technical and non-technical customer and employee inquiries. An ability to handle a varied and fast-paced workload to meet customer requirements for projects. A well-organized and self-directed team player. Ability to work with various computer programs including vendor-based software. We Are Winning When Our expectations are that team members demonstrate our Core Values. Integrity & Respect - Work with the highest ethical standards, interact openly and directly, honor our commitments and value diversity of styles, roles, and perspectives. Teamwork - Actively collaborate with others to solve problems and create opportunities. Devote ourselves to the team's and others' success. Customer Focus - Make customers the starting point for everything we do. Understanding what they want and expect from us will enable us to earn their loyalty. Excellence & Innovation - Continuously elevate our expertise and knowledge to strengthen our competitive advantage; and always look for ways to apply breakthrough ideas. Passion & Energy - Bring passion and energy to our work so that we are "energy givers", enabling us to own and pursue objectives in spite of obstacles and adversity. Fun! - Enjoy what we do and have fun with each other - celebrate a job well done "25% more fun". Benefits and Team Member Perks Positivity, cohesiveness and celebrating a job well done! Competitive compensation and benefits structure within a values-driven culture Work-life balance; generous paid time off program and ability to participate in Flexible Workplace arrangement Comprehensive health insurance coverage 401k with generous company match Intuitive health and wellness program that rewards participation Community involvement and volunteering opportunities Continuous learning through our talent learning management system - Crane University Apply today and join the team at: Crane Engineering is an equal opportunity and affirmative action employer. Qualified applicants will receive consideration for employment without regard to their race, color, religion, national origin, sex, protected veteran status, disability, or any other characteristic protected by law. Crane Engineering also participates in E-Verify to verify identity and employment eligibility. PIbcbd2d3e5-
10/01/2026
Full time
Description: We are a family-oriented Midwest based company in Wisconsin, Minnesota, and Michigan. As a result of our growth, we have a need for an Application Engineer to utilize sales and technical capabilities to grow Crane business within an assigned market. Our mission is to help customers succeed. To achieve our mission, we've instilled a culture and environment that encourages new ideas, promotes experimentation, and inspires innovation. Crane Engineering team members impact the organization's success and are recognized for creating "WOW!" customer experiences. What You Get To Do Develops and maintains the product and application knowledge necessary to service internal and external customers within the assigned market. Supports and keeps sales and management staff informed of major projects; assists office and warehouse staff with application and product information. Provides customer and employee product training and sales call technical support. Provides technical sales and marketing support to Crane customers and staff. Provides the highest quality quotations possible for the specific application as outlined by the customer or Account Manager. Accurately prices, verifies contractual obligations, internal costs, agency sales and stock/drop-shipments. Develops the product and application knowledge necessary to service the needs of our customers and sales staff May perform other duties as assigned. Requirements: What We Need From You Bachelor's degree in a related engineering field preferred. Minimum of one-year related experience and/or training; or equivalent combination of education and experience. An engineering professional with appropriate industry/educational experience. A customer-service focused individual that successfully responds to technical and non-technical customer and employee inquiries. An ability to handle a varied and fast-paced workload to meet customer requirements for projects. A well-organized and self-directed team player. Ability to work with various computer programs including vendor-based software. We Are Winning When Our expectations are that team members demonstrate our Core Values. Integrity & Respect - Work with the highest ethical standards, interact openly and directly, honor our commitments and value diversity of styles, roles, and perspectives. Teamwork - Actively collaborate with others to solve problems and create opportunities. Devote ourselves to the team's and others' success. Customer Focus - Make customers the starting point for everything we do. Understanding what they want and expect from us will enable us to earn their loyalty. Excellence & Innovation - Continuously elevate our expertise and knowledge to strengthen our competitive advantage; and always look for ways to apply breakthrough ideas. Passion & Energy - Bring passion and energy to our work so that we are "energy givers", enabling us to own and pursue objectives in spite of obstacles and adversity. Fun! - Enjoy what we do and have fun with each other - celebrate a job well done "25% more fun". Benefits and Team Member Perks Positivity, cohesiveness and celebrating a job well done! Competitive compensation and benefits structure within a values-driven culture Work-life balance; generous paid time off program and ability to participate in Flexible Workplace arrangement Comprehensive health insurance coverage 401k with generous company match Intuitive health and wellness program that rewards participation Community involvement and volunteering opportunities Continuous learning through our talent learning management system - Crane University Apply today and join the team at: Crane Engineering is an equal opportunity and affirmative action employer. Qualified applicants will receive consideration for employment without regard to their race, color, religion, national origin, sex, protected veteran status, disability, or any other characteristic protected by law. Crane Engineering also participates in E-Verify to verify identity and employment eligibility. PIbcbd2d3e5-
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. Boeing F-22 Mission Systems team is hiring for a Lead Analyst located in Berkeley, MO. As Lead Analyst, you will be responsible for analyzing engineering solutions for the F-22 Raptor, including both enhancing existing field units with the addition of new capabilities as well as architecting and testing completely new systems. In this role you will be joining a cross-functional team to progress our products through the development lifecycle. Position Responsibilities: Leads analysis and translation of complex requirements into system architecture, hardware and software designs and interface specifications. Must be able to work in a fast-paced, collaborative environment, engaging with multiple teams and being responsive to agile requirements and critical path customer need dates. Collaborates with Responsible Engineers, customer technical staff, suppliers, and Lockheed Martin teammates to develop and document complex electronic and electrical system requirements for F-22 avionics sub-systems and weapon system architecture Analyzes and translates requirements into system architecture, hardware and software designs and interface specifications in support of lab and operational environments Reviews test data, including off-nominal data, for accuracy, quality and/or fidelity prior to delivery to customer This position is expected to be 100% onsite. The selected candidate will be required to work onsite at one of the listed location options. This position requires the ability to obtain a US Secret Security Clearance for which the US Government requires US Citizenship A final Secret Clearance Post-Start is required. This position requires ability to obtain program access, for which the U.S. Government requires U.S. Citizenship only. Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering (with a focus in Electrical, Mechanical or Aeronautical), Computer Science, Data Science, Mathematics, Physics, Chemistry Level 5 (Lead): 14+ years of work-related experience with a bachelor's degree or 12+ years of work-related experience with a masters or 9+ years of work-related experience with a PhD Ability to Travel up to 10% Preferred Qualifications (Desired Skills/Experience): Active U.S. Secret Security Clearance or above. Experience in the Aerospace Industry Experience using good communication, analytical, and organizational skills and be able to work in a team environment Experience creating, developing, and/or maintaining engineering processes Experience or training with computer programming languages (e.g., Matlab, Python, Visual Basic) Candidate demonstrates excellent analytical and problem-solving skills Ability to work across functional teams and organizations Conflict of Interest: Successful candidates for this job must satisfy Company's Conflict of Interest (COI) assessment process. Education: Level 5 (Lead): Education/experience typically acquired through advanced technical education from an accredited course of study in engineering, computer science, engineering data science, mathematics, physics or chemistry (e.g. Bachelor) and typically 14 or more years' related work experience or an equivalent combination of technical education and experience or non-US equivalent qualifications. In the USA, ABET accreditation is the preferred, although not required, accreditation standard. Relocation: This position offers relocation based on candidate eligibility. Drug Free Workplace: Boeing is a Drug Free Workplace where post offer applicants and employees are subject to testing for marijuana, cocaine, opioids, amphetamines, PCP, and alcohol when criteria is met as outlined in our policies. Shift: This position is for 1st shift. Pay & Benefits: At Boeing, we strive to deliver a Total Rewards package that will attract, engage and retain the top talent. Elements of the Total Rewards package include competitive base pay and variable compensation opportunities. The Boeing Company also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work. Boeing 401(k) helps you save for your future, with contributions from Boeing that can help you grow your retirement savings. Our best-in-class retirement benefit features: Best in class 401(k) plan: we'll match your contributions dollar for dollar, up to 10% of eligible pay with Immediate 100% vesting Student Loan Match: The Boeing 401(k) Student Loan Match allows eligible enrolled U.S. employees to have their qualified student loan debt payments counted, along with any match-eligible contributions they make, for purposes of determining the Company Match to employees' Boeing 401(k) accounts. The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements. Please note that the salary information shown below is a general guideline only. Pay is based upon candidate experience and qualifications, as well as market and business considerations. Summary pay range: Level 5 (Lead): $164,900 - $223,100 Applications for this position will be accepted until Oct. 07, 2026 Export Control Requirements: This position must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a "U.S. Person" as defined by 22 C.F.R. 120.62 is required. "U.S. Person" includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee. Export Control Details: US based job, US Person required Education Bachelor's Degree or Equivalent Required Relocation This position offers relocation based on candidate eligibility. Security Clearance This position requires the ability to obtain a U.S. Security Clearance for which the U.S. Government requires U.S. Citizenship. An interim and/or final U.S. Secret Clearance Post-Start is required. This position requires ability to obtain program access, for which the U.S. Government requires U.S. Citizenship only. Visa Sponsorship Employer will not sponsor applicants for employment visa status. Shift This position is for 1st shift Equal Opportunity Employer: Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.
09/30/2026
Full time
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. Boeing F-22 Mission Systems team is hiring for a Lead Analyst located in Berkeley, MO. As Lead Analyst, you will be responsible for analyzing engineering solutions for the F-22 Raptor, including both enhancing existing field units with the addition of new capabilities as well as architecting and testing completely new systems. In this role you will be joining a cross-functional team to progress our products through the development lifecycle. Position Responsibilities: Leads analysis and translation of complex requirements into system architecture, hardware and software designs and interface specifications. Must be able to work in a fast-paced, collaborative environment, engaging with multiple teams and being responsive to agile requirements and critical path customer need dates. Collaborates with Responsible Engineers, customer technical staff, suppliers, and Lockheed Martin teammates to develop and document complex electronic and electrical system requirements for F-22 avionics sub-systems and weapon system architecture Analyzes and translates requirements into system architecture, hardware and software designs and interface specifications in support of lab and operational environments Reviews test data, including off-nominal data, for accuracy, quality and/or fidelity prior to delivery to customer This position is expected to be 100% onsite. The selected candidate will be required to work onsite at one of the listed location options. This position requires the ability to obtain a US Secret Security Clearance for which the US Government requires US Citizenship A final Secret Clearance Post-Start is required. This position requires ability to obtain program access, for which the U.S. Government requires U.S. Citizenship only. Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering (with a focus in Electrical, Mechanical or Aeronautical), Computer Science, Data Science, Mathematics, Physics, Chemistry Level 5 (Lead): 14+ years of work-related experience with a bachelor's degree or 12+ years of work-related experience with a masters or 9+ years of work-related experience with a PhD Ability to Travel up to 10% Preferred Qualifications (Desired Skills/Experience): Active U.S. Secret Security Clearance or above. Experience in the Aerospace Industry Experience using good communication, analytical, and organizational skills and be able to work in a team environment Experience creating, developing, and/or maintaining engineering processes Experience or training with computer programming languages (e.g., Matlab, Python, Visual Basic) Candidate demonstrates excellent analytical and problem-solving skills Ability to work across functional teams and organizations Conflict of Interest: Successful candidates for this job must satisfy Company's Conflict of Interest (COI) assessment process. Education: Level 5 (Lead): Education/experience typically acquired through advanced technical education from an accredited course of study in engineering, computer science, engineering data science, mathematics, physics or chemistry (e.g. Bachelor) and typically 14 or more years' related work experience or an equivalent combination of technical education and experience or non-US equivalent qualifications. In the USA, ABET accreditation is the preferred, although not required, accreditation standard. Relocation: This position offers relocation based on candidate eligibility. Drug Free Workplace: Boeing is a Drug Free Workplace where post offer applicants and employees are subject to testing for marijuana, cocaine, opioids, amphetamines, PCP, and alcohol when criteria is met as outlined in our policies. Shift: This position is for 1st shift. Pay & Benefits: At Boeing, we strive to deliver a Total Rewards package that will attract, engage and retain the top talent. Elements of the Total Rewards package include competitive base pay and variable compensation opportunities. The Boeing Company also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work. Boeing 401(k) helps you save for your future, with contributions from Boeing that can help you grow your retirement savings. Our best-in-class retirement benefit features: Best in class 401(k) plan: we'll match your contributions dollar for dollar, up to 10% of eligible pay with Immediate 100% vesting Student Loan Match: The Boeing 401(k) Student Loan Match allows eligible enrolled U.S. employees to have their qualified student loan debt payments counted, along with any match-eligible contributions they make, for purposes of determining the Company Match to employees' Boeing 401(k) accounts. The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements. Please note that the salary information shown below is a general guideline only. Pay is based upon candidate experience and qualifications, as well as market and business considerations. Summary pay range: Level 5 (Lead): $164,900 - $223,100 Applications for this position will be accepted until Oct. 07, 2026 Export Control Requirements: This position must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a "U.S. Person" as defined by 22 C.F.R. 120.62 is required. "U.S. Person" includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee. Export Control Details: US based job, US Person required Education Bachelor's Degree or Equivalent Required Relocation This position offers relocation based on candidate eligibility. Security Clearance This position requires the ability to obtain a U.S. Security Clearance for which the U.S. Government requires U.S. Citizenship. An interim and/or final U.S. Secret Clearance Post-Start is required. This position requires ability to obtain program access, for which the U.S. Government requires U.S. Citizenship only. Visa Sponsorship Employer will not sponsor applicants for employment visa status. Shift This position is for 1st shift Equal Opportunity Employer: Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.
Date Posted: 2026-08-18 Country: United States of America Location: US-AZ-TUCSON- E Hermans Rd BLDG 805 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: Secret - Current Security Clearance Status: Active and existing security clearance required on day 1 We're growing fast and we want you to grow with us! We're expanding our engineering organization dramatically to meet exciting customer demand, and we're actively looking for engineers who bring strong foundational skills and a passion for solving hard problems. Industry experience in defense? Not required - we'll invest in you. If you meet the minimum qualifications, we want to talk. Apply today and take the next step in your engineering career. At RTX, the world largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation and allies defend freedoms and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. Our Senior Embedded Software Engineer is a technical position that works in an Integrated Product Team (IPT) environment to architect, design, implement, test, debug, and deploy Software for Firmware (FPGA) and Hardware solutions that meet current and next generation autonomous avionics systems' needs. Working with a cross-discipline team, the candidate must have experience developing, testing, and integrating software for edge or embedded devices and/or subsystems (like telecom, medical, IoT, automotive, or robotics) where hardware operation, time critical function, functional reliability, mission assurance, and safety might be major concerns. The successful candidate will work with Product Owners, Chief Engineers, Management and other IPT members using Lean and/or Agile practices to ensure that embedded software is designed and developed to reliably operate toward the intended functions. This position is within the Effectors Center of the Software organization, and is an onsite role located in Tucson, AZ. What You Will Do Architecting, designing, implementing, testing, and debugging integrated embedded real-time software within heterogenous systems composed of firmware and hardware. Working within a cross-discipline team to define, refine, and improve product concept, implementation, testability, and guaranteed, measurable quality. Teaching, coaching, and mentoring less experienced staff. Contributing to proposals as well as preliminary and critical design reviews. Ability to obtain program access. What You Will Learn Working across a product line in collaboration with other teams. Qualifications You Must Have Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum of 5 years of prior relevant experience. Experience including at least two of the following: Embedded C++ Software, Embedded Software Security, Software Architecture Design and Implementation. Experience using embedded Real Time Operating Systems (RTOS) (e.g., Green Hills, Integrity, Wind River VxWorks, Linux, etc.) Experience developing complex systems involving the integration of hardware, firmware, and software Active and transferrable final Secret U.S. government issued security clearance is required prior to start date with the ability to obtain program access after start. Qualifications We Prefer Familiarity with reading electrical schematics and relating it to software function Familiarity with reading firmware source like VHDL or Verilog Familiarity with assembly language in at least one processor/controller family Experience using lab instruments like power-supplies, digital multi-meters, oscilloscopes, and logic analyzers Experience with developing device drivers for bare-metal and/or OS applications What We Offer Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation. Relocation Offered Based On Eligibility Learn More & Apply Now! Please consider the following role type definition as you apply for this role: Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. Clearance Information: This position requires a security clearance. DCSA Consolidated Adjudication Services (DCSA CAS), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website here: Location Information: This position is onsite at our campus in beautiful Tucson, AZ. Tucson has a friendly, caring, and laid-back atmosphere, combined with the innovation and energy of a metropolitan region, and recognized as one of America's 10 Best Small Cities. Surrounded by beautiful mountains, colorful Sonoran Desert landscape and majestic saguaro cacti, Tucson is blessed with some of nature's best work. Tucson is known for its bright blue skies, and with more than 310 sunny days per year, Tucson's fantastic weather lets residents enjoy the outdoors year-round. Virtual Fly Over City of Tucson & Community, YouTube Video Links "Raytheon In Tucson": ,-az-location "Tucson is Awesome": "Winter in Tucson": As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 86,800 USD - 165,200 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX 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, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
09/30/2026
Full time
Date Posted: 2026-08-18 Country: United States of America Location: US-AZ-TUCSON- E Hermans Rd BLDG 805 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: Secret - Current Security Clearance Status: Active and existing security clearance required on day 1 We're growing fast and we want you to grow with us! We're expanding our engineering organization dramatically to meet exciting customer demand, and we're actively looking for engineers who bring strong foundational skills and a passion for solving hard problems. Industry experience in defense? Not required - we'll invest in you. If you meet the minimum qualifications, we want to talk. Apply today and take the next step in your engineering career. At RTX, the world largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation and allies defend freedoms and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. Our Senior Embedded Software Engineer is a technical position that works in an Integrated Product Team (IPT) environment to architect, design, implement, test, debug, and deploy Software for Firmware (FPGA) and Hardware solutions that meet current and next generation autonomous avionics systems' needs. Working with a cross-discipline team, the candidate must have experience developing, testing, and integrating software for edge or embedded devices and/or subsystems (like telecom, medical, IoT, automotive, or robotics) where hardware operation, time critical function, functional reliability, mission assurance, and safety might be major concerns. The successful candidate will work with Product Owners, Chief Engineers, Management and other IPT members using Lean and/or Agile practices to ensure that embedded software is designed and developed to reliably operate toward the intended functions. This position is within the Effectors Center of the Software organization, and is an onsite role located in Tucson, AZ. What You Will Do Architecting, designing, implementing, testing, and debugging integrated embedded real-time software within heterogenous systems composed of firmware and hardware. Working within a cross-discipline team to define, refine, and improve product concept, implementation, testability, and guaranteed, measurable quality. Teaching, coaching, and mentoring less experienced staff. Contributing to proposals as well as preliminary and critical design reviews. Ability to obtain program access. What You Will Learn Working across a product line in collaboration with other teams. Qualifications You Must Have Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum of 5 years of prior relevant experience. Experience including at least two of the following: Embedded C++ Software, Embedded Software Security, Software Architecture Design and Implementation. Experience using embedded Real Time Operating Systems (RTOS) (e.g., Green Hills, Integrity, Wind River VxWorks, Linux, etc.) Experience developing complex systems involving the integration of hardware, firmware, and software Active and transferrable final Secret U.S. government issued security clearance is required prior to start date with the ability to obtain program access after start. Qualifications We Prefer Familiarity with reading electrical schematics and relating it to software function Familiarity with reading firmware source like VHDL or Verilog Familiarity with assembly language in at least one processor/controller family Experience using lab instruments like power-supplies, digital multi-meters, oscilloscopes, and logic analyzers Experience with developing device drivers for bare-metal and/or OS applications What We Offer Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation. Relocation Offered Based On Eligibility Learn More & Apply Now! Please consider the following role type definition as you apply for this role: Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. Clearance Information: This position requires a security clearance. DCSA Consolidated Adjudication Services (DCSA CAS), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website here: Location Information: This position is onsite at our campus in beautiful Tucson, AZ. Tucson has a friendly, caring, and laid-back atmosphere, combined with the innovation and energy of a metropolitan region, and recognized as one of America's 10 Best Small Cities. Surrounded by beautiful mountains, colorful Sonoran Desert landscape and majestic saguaro cacti, Tucson is blessed with some of nature's best work. Tucson is known for its bright blue skies, and with more than 310 sunny days per year, Tucson's fantastic weather lets residents enjoy the outdoors year-round. Virtual Fly Over City of Tucson & Community, YouTube Video Links "Raytheon In Tucson": ,-az-location "Tucson is Awesome": "Winter in Tucson": As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 86,800 USD - 165,200 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX 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, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
Project Manager RGS Products, Inc. is an architectural railing and metals manufacturer that provides customers with custom-designed balcony and stairway railings. We are seeking an experienced Project Manager to join our growing team. The Project Manager will coordinate and manage individual architectural projects. This is not an IT Project Manager position. Candidates seeking IT-focused project management roles need not apply. Job Type: Full-time Benefits: 401(k) 401(k) Matching Dental Insurance Health Insurance Paid Life Insurance Paid Long-Term Disability Paid Time Off Personal Time Vision Insurance Experience: Construction Experience: 1 year (Preferred) Project Management: 1 year (Preferred) Management experience leading multidisciplinary project teams: 1 year (Preferred) Blueprint Reading a must Responsibilities: Oversee various aspects of engineering, workflow, budget, field verification, and scheduling of the assigned project from start to finish. Serve as the liaison for all communications between RGS and the client and installers. Lead project meetings. Collaborate with project staff to create a strategic action plan and project estimates of products, time, install labor, and other resources required to complete the project Facilitate and contribute to the development of project plans and designs Compare the actual products of the project to estimates. Recommend and/or analyze materials, equipment, and practices used in the project. Ensure compliance with codes, guidelines, best practices, jobsite safety, and policies. Collaborate with project engineers and senior management to identify and troubleshoot problems that arise. Assess the competence, capabilities, and sufficiency of resources for subcontract installers. Manage and communicate requirements for Change Orders to project team and clients. Coordinate with Operations Manager on production scheduling and priorities. Coordinate with Engineering on submittals, drawing timelines, and material BOMs. Coordinate with Purchasing for material needs and material shipping timelines. Coordinate with Shipping on finished product delivery timelines. Coordinate with Installers on delivery dates and client-set timelines for installation completeness. Coordinate with clients/GCs on all project timelines. Monitor project milestones and critical path activities, identifying schedule risks affecting fabrication, delivery, and installation. Ensure project deliverables meet quality standards and customer specifications. Support corrective actions and continuous improvement. Promote OSHA and company safety compliance. Coordinate punch lists and project closeout documentation. Prepare periodic progress reports for leadership. Perform other related duties as assigned. Required Skills/Abilities: Efficient verbal and written communication skills Efficient cost containment skills Efficient organizational skills and attention to detail Efficient time management skills with a proven ability to meet deadlines Strong analytical and problem-solving skills Strong supervisory and leadership skills Proficient with Microsoft Office Suite and related software (AutoCAD, Bluebeam, etc.) Physical Requirements: Extensive travel required (must be able to travel to job sites with short notice) Prolonged periods sitting at a desk and working on a computer Must be able to lift up to 15 pounds at times Compensation details: 0 Yearly Salary PIce3410d264e2-9168
09/30/2026
Full time
Project Manager RGS Products, Inc. is an architectural railing and metals manufacturer that provides customers with custom-designed balcony and stairway railings. We are seeking an experienced Project Manager to join our growing team. The Project Manager will coordinate and manage individual architectural projects. This is not an IT Project Manager position. Candidates seeking IT-focused project management roles need not apply. Job Type: Full-time Benefits: 401(k) 401(k) Matching Dental Insurance Health Insurance Paid Life Insurance Paid Long-Term Disability Paid Time Off Personal Time Vision Insurance Experience: Construction Experience: 1 year (Preferred) Project Management: 1 year (Preferred) Management experience leading multidisciplinary project teams: 1 year (Preferred) Blueprint Reading a must Responsibilities: Oversee various aspects of engineering, workflow, budget, field verification, and scheduling of the assigned project from start to finish. Serve as the liaison for all communications between RGS and the client and installers. Lead project meetings. Collaborate with project staff to create a strategic action plan and project estimates of products, time, install labor, and other resources required to complete the project Facilitate and contribute to the development of project plans and designs Compare the actual products of the project to estimates. Recommend and/or analyze materials, equipment, and practices used in the project. Ensure compliance with codes, guidelines, best practices, jobsite safety, and policies. Collaborate with project engineers and senior management to identify and troubleshoot problems that arise. Assess the competence, capabilities, and sufficiency of resources for subcontract installers. Manage and communicate requirements for Change Orders to project team and clients. Coordinate with Operations Manager on production scheduling and priorities. Coordinate with Engineering on submittals, drawing timelines, and material BOMs. Coordinate with Purchasing for material needs and material shipping timelines. Coordinate with Shipping on finished product delivery timelines. Coordinate with Installers on delivery dates and client-set timelines for installation completeness. Coordinate with clients/GCs on all project timelines. Monitor project milestones and critical path activities, identifying schedule risks affecting fabrication, delivery, and installation. Ensure project deliverables meet quality standards and customer specifications. Support corrective actions and continuous improvement. Promote OSHA and company safety compliance. Coordinate punch lists and project closeout documentation. Prepare periodic progress reports for leadership. Perform other related duties as assigned. Required Skills/Abilities: Efficient verbal and written communication skills Efficient cost containment skills Efficient organizational skills and attention to detail Efficient time management skills with a proven ability to meet deadlines Strong analytical and problem-solving skills Strong supervisory and leadership skills Proficient with Microsoft Office Suite and related software (AutoCAD, Bluebeam, etc.) Physical Requirements: Extensive travel required (must be able to travel to job sites with short notice) Prolonged periods sitting at a desk and working on a computer Must be able to lift up to 15 pounds at times Compensation details: 0 Yearly Salary PIce3410d264e2-9168
RELOCATION ASSISTANCE: Relocation assistance may be available CLEARANCE REQUIRED FOR START: Yes CLEARANCE TYPE: Top Secret TRAVEL: Yes, 10% of the Time Description At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people's lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation's history - from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future, and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work - and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they're making history. Northrop Grumman Aeronautics Systems is looking to add Staff Embedded & Real Time Software Engineer to join our team of qualified, diverse individuals within our Software organization. This position can be filled on site in San Diego or Palmdale, California. In this role you will design, develop, integrate and test software (SW) for our end-user customers and businesses. You will be responsible for the design, architecture, development, and administration of embedded and real time systems. You will work with multi-disciplinary teams, such as Systems Engineering, Cloud & Application, Test Automation, DevSecOps and Systems Test, in an Agile SW development environment. You will analyze system capabilities to resolve problems on program intent, output requirements, input data acquisition, programming techniques and controls. Duties and Responsibilities include, but are not limited to: • Work in a fast-paced environment with high expectations, significantly diverse assignments, and collaborative team settings across all levels • Participate in the full SW development life cycle including requirements, design, implementation, qualification, and delivery of SW products to our customers • Operate in an embedded technology development environment working with real time operating systems for use in flight and mission critical systems • Work in Agile Scrum teams to develop SW products for multiple SW baselines Basic Qualifications: •Bachelor's Degree in a STEM (Science, Technology, Engineering or Mathematics) discipline and 12 years of related engineering experience; OR a Master's degree in a STEM discipline and 10 years of related engineering experience; OR a PhD in a STEM discipline and 8 years of related engineering experience. •Experience with the full SW Development Life Cycle (SDLC) •Experience with the following programming and scripting languages: C, C++, and Python •Experience with developing multi-threaded SW used for one of the following real-time applications: flight critical SW, Safety critical SW, medical SW, or mission critical SW •Experience with two of the following tools: Jira, Crucible, Bitbucket, Subversion, Bamboo, Jenkins, Sonarqube, Fortify, or Coverity •Experience with Agile SW development, embedded system programming, SWintegration and testing •Familiarity with at least two of the data interfaces: 1553, 1394, 422, 429, Serial, CAN, Discrete & Analog I/O, Ethernet/IP, or Fibre Channel •Excellent communication, interpersonal skills, and the ability to interface with all levels of employees and management •Recent experience and proficiency in software architecture, design, implementation, integration, and debugging C++ SW running on Real-Time Operating systems such as: VxWorks, Integrity, AND/OR Real Time Embedded Linux. •Ability to collaborate with systems engineers, hardware designers and integration/test engineers to develop and maintain complex SW systems •Active in-Scope, U.S. Government Top Secret clearance. •Ability to obtain and maintain initial Special Program Access (SAP/PAR). This SAP/PAR must be obtained prior to commencement of employment and must be obtained within a reasonable amount of time as determined by the company to meet its business needs. (Of note: SAP/PAR will be upgraded once selected candidate is in role. Therefore, candidate must also be able to obtain and maintain upgraded SAP/PAR.) Preferred Qualifications: •Current applicable SAP access •Basic understanding of PowerPC or ARM assembly •Able to read assembly and use CPU documentation to understand behaviors •Can identify moderate execution defects by only reading code • Understanding of how their embedded SW component will fit into a larger SW system / architecture • Able to document, capture, and present software architectural components in modeling or diagramming tools • Familiarity with OS partitions and isolation architectures • High level understanding of architectural requirements for airworthiness certifications •Strong understanding of the following: o layered SW architectures (e.g. Application, System, Device) o SW hardware interfaces and where they exist in the architecture o Event driven (Interrupts), polling and synchronous SW architectures • Recent experience and proficiency with SW change control, change management, static analysis, and CI/CD tools such as: Atlassian tool suite, Jira, GitHub, GitLab, SonarQube, Coverity, AND/OR Jenkins within the last three years. • Experience with Static & Dynamic Code Analysis Primary Level Salary Range: $177,000.00 - $265,600.00 The above salary range represents a general guideline; however, Northrop Grumman considers a number of factors when determining base salary offers such as the scope and responsibilities of the position and the candidate's experience, education, skills and current market conditions. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay. Annual bonuses are designed to reward individual contributions as well as allow employees to share in company results. Employees in Vice President or Director positions may be eligible for Long Term Incentives. In addition, Northrop Grumman provides a variety of benefits including health insurance coverage, life and disability insurance, savings plan, Company paid holidays and paid time off (PTO) for vacation and/or personal business. The application period for the job is estimated to be 20 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. Northrop Grumman is an Equal Opportunity Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. For our complete EEO and pay transparency statement, please visit U.S. Citizenship is required for all positions with a government clearance and certain other restricted positions.
09/30/2026
Full time
RELOCATION ASSISTANCE: Relocation assistance may be available CLEARANCE REQUIRED FOR START: Yes CLEARANCE TYPE: Top Secret TRAVEL: Yes, 10% of the Time Description At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people's lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation's history - from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future, and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work - and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they're making history. Northrop Grumman Aeronautics Systems is looking to add Staff Embedded & Real Time Software Engineer to join our team of qualified, diverse individuals within our Software organization. This position can be filled on site in San Diego or Palmdale, California. In this role you will design, develop, integrate and test software (SW) for our end-user customers and businesses. You will be responsible for the design, architecture, development, and administration of embedded and real time systems. You will work with multi-disciplinary teams, such as Systems Engineering, Cloud & Application, Test Automation, DevSecOps and Systems Test, in an Agile SW development environment. You will analyze system capabilities to resolve problems on program intent, output requirements, input data acquisition, programming techniques and controls. Duties and Responsibilities include, but are not limited to: • Work in a fast-paced environment with high expectations, significantly diverse assignments, and collaborative team settings across all levels • Participate in the full SW development life cycle including requirements, design, implementation, qualification, and delivery of SW products to our customers • Operate in an embedded technology development environment working with real time operating systems for use in flight and mission critical systems • Work in Agile Scrum teams to develop SW products for multiple SW baselines Basic Qualifications: •Bachelor's Degree in a STEM (Science, Technology, Engineering or Mathematics) discipline and 12 years of related engineering experience; OR a Master's degree in a STEM discipline and 10 years of related engineering experience; OR a PhD in a STEM discipline and 8 years of related engineering experience. •Experience with the full SW Development Life Cycle (SDLC) •Experience with the following programming and scripting languages: C, C++, and Python •Experience with developing multi-threaded SW used for one of the following real-time applications: flight critical SW, Safety critical SW, medical SW, or mission critical SW •Experience with two of the following tools: Jira, Crucible, Bitbucket, Subversion, Bamboo, Jenkins, Sonarqube, Fortify, or Coverity •Experience with Agile SW development, embedded system programming, SWintegration and testing •Familiarity with at least two of the data interfaces: 1553, 1394, 422, 429, Serial, CAN, Discrete & Analog I/O, Ethernet/IP, or Fibre Channel •Excellent communication, interpersonal skills, and the ability to interface with all levels of employees and management •Recent experience and proficiency in software architecture, design, implementation, integration, and debugging C++ SW running on Real-Time Operating systems such as: VxWorks, Integrity, AND/OR Real Time Embedded Linux. •Ability to collaborate with systems engineers, hardware designers and integration/test engineers to develop and maintain complex SW systems •Active in-Scope, U.S. Government Top Secret clearance. •Ability to obtain and maintain initial Special Program Access (SAP/PAR). This SAP/PAR must be obtained prior to commencement of employment and must be obtained within a reasonable amount of time as determined by the company to meet its business needs. (Of note: SAP/PAR will be upgraded once selected candidate is in role. Therefore, candidate must also be able to obtain and maintain upgraded SAP/PAR.) Preferred Qualifications: •Current applicable SAP access •Basic understanding of PowerPC or ARM assembly •Able to read assembly and use CPU documentation to understand behaviors •Can identify moderate execution defects by only reading code • Understanding of how their embedded SW component will fit into a larger SW system / architecture • Able to document, capture, and present software architectural components in modeling or diagramming tools • Familiarity with OS partitions and isolation architectures • High level understanding of architectural requirements for airworthiness certifications •Strong understanding of the following: o layered SW architectures (e.g. Application, System, Device) o SW hardware interfaces and where they exist in the architecture o Event driven (Interrupts), polling and synchronous SW architectures • Recent experience and proficiency with SW change control, change management, static analysis, and CI/CD tools such as: Atlassian tool suite, Jira, GitHub, GitLab, SonarQube, Coverity, AND/OR Jenkins within the last three years. • Experience with Static & Dynamic Code Analysis Primary Level Salary Range: $177,000.00 - $265,600.00 The above salary range represents a general guideline; however, Northrop Grumman considers a number of factors when determining base salary offers such as the scope and responsibilities of the position and the candidate's experience, education, skills and current market conditions. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay. Annual bonuses are designed to reward individual contributions as well as allow employees to share in company results. Employees in Vice President or Director positions may be eligible for Long Term Incentives. In addition, Northrop Grumman provides a variety of benefits including health insurance coverage, life and disability insurance, savings plan, Company paid holidays and paid time off (PTO) for vacation and/or personal business. The application period for the job is estimated to be 20 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. Northrop Grumman is an Equal Opportunity Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. For our complete EEO and pay transparency statement, please visit U.S. Citizenship is required for all positions with a government clearance and certain other restricted positions.
Date Posted: 2026-08-17 Country: United States of America Location: US-AZ-TUCSON- E Hermans Rd BLDG 805 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: Secret - Current Security Clearance Status: Active and existing security clearance required on day 1 At RTX, the world largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation and allies defend freedoms and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. The Software organization develops software applications, including integration and test on missiles, launchers, radars, naval systems, fire control and other complex systems. Our precision software and firmware integrate operating systems, device drivers, networking, and control software to bring together sensor, guidance, and flight control processing features to complete the mission. The Software org is made up of several Centers located across the country, responsible for all aspects of the software development lifecycle. Our 4000+ software engineers design, develop, and build innovative solutions for our customers. Join our fast-paced agile teams on the leading edge of technology. As part of the Software Engineering Directorate's (SWE) Effectors Center (EC) team, you will be an integral part of helping Raytheon further our vision to be the global leader in core and next-generation weapon and security solutions. By any measure, Raytheon is an exciting and rewarding place to work. We pride ourselves on developing mission-driven, world-class talent. The result is a workforce that takes pride in the company and consistently delivers superior solutions. Our Senior Principal Embedded Real-Time Software Developer/Integrator is a technical position that works in an Integrated Product Team (IPT) environment to architect, design, implement, test, debug, and deploy Software for Firmware (FPGA) and Hardware solutions that meet current and next generation autonomous avionics systems' needs. Working with a cross-discipline team, the candidate must have experience developing, testing, and integrating software for edge or embedded devices and/or subsystems (like telecom, medical, IoT, automotive, or robotics) where hardware operation, time critical function, functional reliability, mission assurance, and safety might be major concerns. The successful candidate will work with Product Owners, Chief Engineers, Management and other IPT members using Lean and/or Agile practices to ensure that embedded software is designed and developed to reliably operate toward the intended functions. This position is within the Effectors Center of the Software organization, and is an onsite role located in Tucson, AZ. What You Will Do Architecting, designing, implementing, testing, and debugging integrated embedded real-time software within heterogenous systems composed of firmware and hardware Working within a cross-discipline team to define, refine, and improve product concept, implementation, testability, and guaranteed, measurable quality Teaching, coaching, and mentoring less experienced staff Contributing to proposals as well as preliminary and critical design reviews Ability to obtain program access What You Will Learn Working across a product line in collaboration with other teams Qualifications You Must Have Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum of 10 years of prior relevant experience. Experience including at least two of the following: Embedded C++ Software, Embedded Software Security, Software Architecture Design and Implementation. Experience using embedded Real Time Operating Systems (RTOS) (e.g., Green Hills, Integrity, Wind River VxWorks, Linux, etc.). Experience developing complex systems involving the integration of hardware, firmware, and software. Active and transferable Secret U.S. government issued security clearance is required prior to start date with the ability to obtain program access after start. Qualifications We Prefer Familiarity with rate monotonic theory, practice, and limitations Familiarity with layered architectural principles, and their limitations Familiarity with reading electrical schematics and relating it to software function Familiarity with reading firmware source like VHDL or Verilog Familiarity with assembly language in at least one processor/controller family Experience using lab instruments like power-supplies, digital multi-meters, oscilloscopes, and logic analyzers Experience with developing device drivers for bare-metal and/or OS applications Experience leading engineering teams in delivering systems (of various size) involving the integration of hardware and software. What We Offer Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation. Relocation Offered Based On Eligibility Learn More & Apply Now! Please consider the following role type definition as you apply for this role: Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. Clearance Information: This position requires a security clearance. DCSA Consolidated Adjudication Services (DCSA CAS), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website here: Location Information: This position is onsite at our campus in beautiful Tucson, AZ. Tucson has a friendly, caring, and laid-back atmosphere, combined with the innovation and energy of a metropolitan region, and recognized as one of America's 10 Best Small Cities. Surrounded by beautiful mountains, colorful Sonoran Desert landscape and majestic saguaro cacti, Tucson is blessed with some of nature's best work. Tucson is known for its bright blue skies, and with more than 310 sunny days per year, Tucson's fantastic weather lets residents enjoy the outdoors year-round. Virtual Fly Over City of Tucson & Community, YouTube Video Links "Raytheon In Tucson": ,-az-location "Tucson is Awesome": "Winter in Tucson": As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 132,400 USD - 251,600 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. . click apply for full job details
09/30/2026
Full time
Date Posted: 2026-08-17 Country: United States of America Location: US-AZ-TUCSON- E Hermans Rd BLDG 805 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: Secret - Current Security Clearance Status: Active and existing security clearance required on day 1 At RTX, the world largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation and allies defend freedoms and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. The Software organization develops software applications, including integration and test on missiles, launchers, radars, naval systems, fire control and other complex systems. Our precision software and firmware integrate operating systems, device drivers, networking, and control software to bring together sensor, guidance, and flight control processing features to complete the mission. The Software org is made up of several Centers located across the country, responsible for all aspects of the software development lifecycle. Our 4000+ software engineers design, develop, and build innovative solutions for our customers. Join our fast-paced agile teams on the leading edge of technology. As part of the Software Engineering Directorate's (SWE) Effectors Center (EC) team, you will be an integral part of helping Raytheon further our vision to be the global leader in core and next-generation weapon and security solutions. By any measure, Raytheon is an exciting and rewarding place to work. We pride ourselves on developing mission-driven, world-class talent. The result is a workforce that takes pride in the company and consistently delivers superior solutions. Our Senior Principal Embedded Real-Time Software Developer/Integrator is a technical position that works in an Integrated Product Team (IPT) environment to architect, design, implement, test, debug, and deploy Software for Firmware (FPGA) and Hardware solutions that meet current and next generation autonomous avionics systems' needs. Working with a cross-discipline team, the candidate must have experience developing, testing, and integrating software for edge or embedded devices and/or subsystems (like telecom, medical, IoT, automotive, or robotics) where hardware operation, time critical function, functional reliability, mission assurance, and safety might be major concerns. The successful candidate will work with Product Owners, Chief Engineers, Management and other IPT members using Lean and/or Agile practices to ensure that embedded software is designed and developed to reliably operate toward the intended functions. This position is within the Effectors Center of the Software organization, and is an onsite role located in Tucson, AZ. What You Will Do Architecting, designing, implementing, testing, and debugging integrated embedded real-time software within heterogenous systems composed of firmware and hardware Working within a cross-discipline team to define, refine, and improve product concept, implementation, testability, and guaranteed, measurable quality Teaching, coaching, and mentoring less experienced staff Contributing to proposals as well as preliminary and critical design reviews Ability to obtain program access What You Will Learn Working across a product line in collaboration with other teams Qualifications You Must Have Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum of 10 years of prior relevant experience. Experience including at least two of the following: Embedded C++ Software, Embedded Software Security, Software Architecture Design and Implementation. Experience using embedded Real Time Operating Systems (RTOS) (e.g., Green Hills, Integrity, Wind River VxWorks, Linux, etc.). Experience developing complex systems involving the integration of hardware, firmware, and software. Active and transferable Secret U.S. government issued security clearance is required prior to start date with the ability to obtain program access after start. Qualifications We Prefer Familiarity with rate monotonic theory, practice, and limitations Familiarity with layered architectural principles, and their limitations Familiarity with reading electrical schematics and relating it to software function Familiarity with reading firmware source like VHDL or Verilog Familiarity with assembly language in at least one processor/controller family Experience using lab instruments like power-supplies, digital multi-meters, oscilloscopes, and logic analyzers Experience with developing device drivers for bare-metal and/or OS applications Experience leading engineering teams in delivering systems (of various size) involving the integration of hardware and software. What We Offer Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation. Relocation Offered Based On Eligibility Learn More & Apply Now! Please consider the following role type definition as you apply for this role: Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. Clearance Information: This position requires a security clearance. DCSA Consolidated Adjudication Services (DCSA CAS), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website here: Location Information: This position is onsite at our campus in beautiful Tucson, AZ. Tucson has a friendly, caring, and laid-back atmosphere, combined with the innovation and energy of a metropolitan region, and recognized as one of America's 10 Best Small Cities. Surrounded by beautiful mountains, colorful Sonoran Desert landscape and majestic saguaro cacti, Tucson is blessed with some of nature's best work. Tucson is known for its bright blue skies, and with more than 310 sunny days per year, Tucson's fantastic weather lets residents enjoy the outdoors year-round. Virtual Fly Over City of Tucson & Community, YouTube Video Links "Raytheon In Tucson": ,-az-location "Tucson is Awesome": "Winter in Tucson": As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 132,400 USD - 251,600 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. . click apply for full job details
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Network Security Systems Manager - Q Clearance Required Summary: We are seeking an experienced Network Security Systems Manager to lead the administration, security, and operations of network security systems supporting LAN/WAN environments. The ideal candidate will have extensive experience managing enterprise network security infrastructure, leading technical security teams, and implementing security policies, controls, and best practices. This role requires strong technical judgment, leadership skills, and the ability to plan, prioritize, and execute network security initiatives in a complex government environment. Location: Washington, DC or Gaithersburg, MD Salary: Up to $178,000 Clearance: Active Q Clearance Required Education: Bachelor's degree from an accredited university or college in Information Technology with an emphasis in Cybersecurity or Information Assurance, Computer Science, or a related field. Required Certifications: One or more of the following certifications or equivalent: GIAC Information Security Professional (GISP) ISC2 Certified Information Systems Security Professional (CISSP) GIAC Security Essentials (GSEC) Experience: Minimum of five (5) years of experience managing network security systems in LAN/WAN environments. Experience leading network security administration staff and technical teams. Experience managing enterprise network security infrastructure, systems, and technologies. Broad knowledge of network security concepts, practices, principles, and procedures. Experience planning, implementing, and maintaining network security solutions. Experience identifying and addressing network security risks, vulnerabilities, and operational issues. Ability to apply extensive technical experience and sound judgment to plan and accomplish organizational goals. Experience developing and implementing security policies, procedures, standards, and controls. Strong understanding of network security architecture, access controls, firewalls, intrusion detection/prevention, VPNs, and secure network communications. Experience supporting security monitoring, incident response, vulnerability management, and network security assessments. Ability to lead technical teams, prioritize workload, manage competing requirements, and communicate effectively with technical and management stakeholders. Preferred Certifications: Certified Information Security Manager (CISM) ISC2 Certified Cloud Security Professional (CCSP) GIAC Network Forensic Analyst (GNFA) GIAC Certified Intrusion Analyst (GCIA) GIAC Certified Incident Handler (GCIH) GIAC Certified Firewall Analyst (GCFW) Cisco Certified Network Professional - Security (CCNP Security) Fortinet Certified Professional or equivalent Fortinet certification Palo Alto Networks Certified Network Security Engineer (PCNSE) or equivalent CompTIA Security+ CompTIA CySA+ ISACA Certified in Risk and Information Systems Control (CRISC) Certified Ethical Hacker (CEH) Juniper Networks security certification or equivalent ActioNet is a CMMI-DEV Level 4, CMMI-SVC Level 4, ISO 20000, ISO 27001, ISO 9001, HDI-certified, woman-owned IT Solutions Provider with strong qualifications and expertise in Agile Software Engineering, Cloud Solutions, Cyber Security and IT Managed Services. With 24+ years of stellar past performance, ActioNet is the premier Trusted Innogrator! Why ActioNet? At ActioNet, our Passion for Quality is at the heart of everything we do: We are committed to make ActioNet a great place to work and continue to invest in our ActioNeters We are committed to our customers by driving and sustaining Service Delivery Excellence We are committed to give back to our Community, help others and make the world a better place for our next generation ActioNet is proud to be named as a Top Workplace for the ninth year in a row (2014 - 2022). We have 98% of Customer retention rate. We are passionate about the inspirational missions of our customers and we entrust our employees and teams to deliver exceptional performance to enable the safety, security, health and well-being of our nation. What's in It For You? As an ActioNeter, you get to be part of exceptional team and a corporate culture that nurtures mutual success for our customers, employees and our communities. We give you the tools to be successful; all you need to do is bring your best ideas, your energy and a desire to develop your skills, experience and career. Are you ready to make a difference? ActioNet is an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Direct Applicants, only. No Agencies, No third-party recruiters, please
09/30/2026
Full time
Job Description Job Description Network Security Systems Manager - Q Clearance Required Summary: We are seeking an experienced Network Security Systems Manager to lead the administration, security, and operations of network security systems supporting LAN/WAN environments. The ideal candidate will have extensive experience managing enterprise network security infrastructure, leading technical security teams, and implementing security policies, controls, and best practices. This role requires strong technical judgment, leadership skills, and the ability to plan, prioritize, and execute network security initiatives in a complex government environment. Location: Washington, DC or Gaithersburg, MD Salary: Up to $178,000 Clearance: Active Q Clearance Required Education: Bachelor's degree from an accredited university or college in Information Technology with an emphasis in Cybersecurity or Information Assurance, Computer Science, or a related field. Required Certifications: One or more of the following certifications or equivalent: GIAC Information Security Professional (GISP) ISC2 Certified Information Systems Security Professional (CISSP) GIAC Security Essentials (GSEC) Experience: Minimum of five (5) years of experience managing network security systems in LAN/WAN environments. Experience leading network security administration staff and technical teams. Experience managing enterprise network security infrastructure, systems, and technologies. Broad knowledge of network security concepts, practices, principles, and procedures. Experience planning, implementing, and maintaining network security solutions. Experience identifying and addressing network security risks, vulnerabilities, and operational issues. Ability to apply extensive technical experience and sound judgment to plan and accomplish organizational goals. Experience developing and implementing security policies, procedures, standards, and controls. Strong understanding of network security architecture, access controls, firewalls, intrusion detection/prevention, VPNs, and secure network communications. Experience supporting security monitoring, incident response, vulnerability management, and network security assessments. Ability to lead technical teams, prioritize workload, manage competing requirements, and communicate effectively with technical and management stakeholders. Preferred Certifications: Certified Information Security Manager (CISM) ISC2 Certified Cloud Security Professional (CCSP) GIAC Network Forensic Analyst (GNFA) GIAC Certified Intrusion Analyst (GCIA) GIAC Certified Incident Handler (GCIH) GIAC Certified Firewall Analyst (GCFW) Cisco Certified Network Professional - Security (CCNP Security) Fortinet Certified Professional or equivalent Fortinet certification Palo Alto Networks Certified Network Security Engineer (PCNSE) or equivalent CompTIA Security+ CompTIA CySA+ ISACA Certified in Risk and Information Systems Control (CRISC) Certified Ethical Hacker (CEH) Juniper Networks security certification or equivalent ActioNet is a CMMI-DEV Level 4, CMMI-SVC Level 4, ISO 20000, ISO 27001, ISO 9001, HDI-certified, woman-owned IT Solutions Provider with strong qualifications and expertise in Agile Software Engineering, Cloud Solutions, Cyber Security and IT Managed Services. With 24+ years of stellar past performance, ActioNet is the premier Trusted Innogrator! Why ActioNet? At ActioNet, our Passion for Quality is at the heart of everything we do: We are committed to make ActioNet a great place to work and continue to invest in our ActioNeters We are committed to our customers by driving and sustaining Service Delivery Excellence We are committed to give back to our Community, help others and make the world a better place for our next generation ActioNet is proud to be named as a Top Workplace for the ninth year in a row (2014 - 2022). We have 98% of Customer retention rate. We are passionate about the inspirational missions of our customers and we entrust our employees and teams to deliver exceptional performance to enable the safety, security, health and well-being of our nation. What's in It For You? As an ActioNeter, you get to be part of exceptional team and a corporate culture that nurtures mutual success for our customers, employees and our communities. We give you the tools to be successful; all you need to do is bring your best ideas, your energy and a desire to develop your skills, experience and career. Are you ready to make a difference? ActioNet is an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Direct Applicants, only. No Agencies, No third-party recruiters, please
Job Description Job Description Fortinet NSE Certification is Must Rate: $52/hr on W2 or $60/hr on C2C we have an opening for an Expert Network Administrator HBITS-07-14631 DURATION: 30 Months LOCATION: New York, NY - Hybrid Locals ONLY with Local ID required Supporting the Department's hybrid security infrastructure. Qualifications Network Administrator- Maintains computer infrastructures with emphasis on networking. Key areas of expertise are with on-site servers, software-network interactions and network integrity/resilience. Expert- 84+ months: Candidate is able to provide guidance to large teams and/or has extensive industry experience and is considered at the top of his/her field. 84 months of Administration & Engineering experience with Enterprise firewalls 84 months of experience with Implementing security solutions covering Threat Prevention, URL Filtering, and SSL Decryption 84 months of experience with firewall management systems 84 months of experience with Intrusion protection and prevention systems. 84 months of experience with configuring routes and encrypted tunnels between remote locations 84 months of experience with developing and deploying BGP routing solutions. 84 months of experience with high availability network technologies and configurations 84 months of experience with written communications skills. Including creating network diagrams, and documenting changes and composing orders of operations for tasks. 84 months of experience implementing, and integrating network solutions based on Cisco LAN/WAN. 84 months of experience configuring and installing various network devices and services (e.g., routers, switches, firewalls, VPN). 84 months of network experience and understanding of: Local Area Networks, Wide Area Networks, TCP/IP, switching and routing protocols. 84 months of experience mentoring staff Fortinet NSE Certification Day-to-Day tasks Install, configure, and manage firewall Security appliances in a high availability architecture. Patch and maintain the appliance with all relevant security and upgrade software. Implement changes to address new initiatives or application requirements as needed. Monitor and support the VPN solution to ensure high availability in a secure manner. Configure and Manage Azure based networking and security. Create Firewall rules, Vnets, express routes as required. Monitor security center for compliance for all relevant security benchmarks and standards. Assist in creating ARM as needed. Produce network diagrams and documentation as well as document standards, policies, and procedures. Mentor staff Powered by JazzHR HZ7aXNvYRD
09/30/2026
Full time
Job Description Job Description Fortinet NSE Certification is Must Rate: $52/hr on W2 or $60/hr on C2C we have an opening for an Expert Network Administrator HBITS-07-14631 DURATION: 30 Months LOCATION: New York, NY - Hybrid Locals ONLY with Local ID required Supporting the Department's hybrid security infrastructure. Qualifications Network Administrator- Maintains computer infrastructures with emphasis on networking. Key areas of expertise are with on-site servers, software-network interactions and network integrity/resilience. Expert- 84+ months: Candidate is able to provide guidance to large teams and/or has extensive industry experience and is considered at the top of his/her field. 84 months of Administration & Engineering experience with Enterprise firewalls 84 months of experience with Implementing security solutions covering Threat Prevention, URL Filtering, and SSL Decryption 84 months of experience with firewall management systems 84 months of experience with Intrusion protection and prevention systems. 84 months of experience with configuring routes and encrypted tunnels between remote locations 84 months of experience with developing and deploying BGP routing solutions. 84 months of experience with high availability network technologies and configurations 84 months of experience with written communications skills. Including creating network diagrams, and documenting changes and composing orders of operations for tasks. 84 months of experience implementing, and integrating network solutions based on Cisco LAN/WAN. 84 months of experience configuring and installing various network devices and services (e.g., routers, switches, firewalls, VPN). 84 months of network experience and understanding of: Local Area Networks, Wide Area Networks, TCP/IP, switching and routing protocols. 84 months of experience mentoring staff Fortinet NSE Certification Day-to-Day tasks Install, configure, and manage firewall Security appliances in a high availability architecture. Patch and maintain the appliance with all relevant security and upgrade software. Implement changes to address new initiatives or application requirements as needed. Monitor and support the VPN solution to ensure high availability in a secure manner. Configure and Manage Azure based networking and security. Create Firewall rules, Vnets, express routes as required. Monitor security center for compliance for all relevant security benchmarks and standards. Assist in creating ARM as needed. Produce network diagrams and documentation as well as document standards, policies, and procedures. Mentor staff Powered by JazzHR HZ7aXNvYRD
Senior Staff Machine Learning Engineer 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. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent 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 10 years of experience programming with Python, Java, Golang, or C++ At least 8 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 8 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 8 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 5+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 7+ years of experience optimizing ML algorithms, configurations, and infrastructure 7+ 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) 9+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Experience shaping long term cross-organizational machine learning strategy Ability to communicate complex technical concepts clearly to executive leadership Recognized leader in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. 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: $314,800 - $359,300 for Sr. Staff Machine Learning Engineer Plano, TX: $286,200 - $326,700 for Sr. Staff Machine Learning Engineer 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).
09/30/2026
Full time
Senior Staff Machine Learning Engineer 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. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent 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 10 years of experience programming with Python, Java, Golang, or C++ At least 8 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 8 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 8 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 5+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 7+ years of experience optimizing ML algorithms, configurations, and infrastructure 7+ 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) 9+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Experience shaping long term cross-organizational machine learning strategy Ability to communicate complex technical concepts clearly to executive leadership Recognized leader in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. 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: $314,800 - $359,300 for Sr. Staff Machine Learning Engineer Plano, TX: $286,200 - $326,700 for Sr. Staff Machine Learning Engineer 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).
Staff Machine Learning Engineer 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. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent 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 8 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 5 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 architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ 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) 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. 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: $269,100 - $307,200 for Staff Machine Learning Engineer Plano, TX: $244,700 - $279,200 for Staff Machine Learning Engineer 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).
09/30/2026
Full time
Staff Machine Learning Engineer 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. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent 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 8 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 5 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 architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ 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) 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. 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: $269,100 - $307,200 for Staff Machine Learning Engineer Plano, TX: $244,700 - $279,200 for Staff Machine Learning Engineer 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).
Staff AI Engineer - Enterprise Analysis Platform (Remote Eligible) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is currently working on building a new AI native platform to empower our broader business community to execute AI assisted analysis across our many lines of business. We are building an AI native, at-scale (15,000+ users at destination), enterprise platform intertwining deterministic code with non-deterministic reasoning systems as well as both frontier and home grown AI models, to transform how our company works in the data analysis space. What You'll Do: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more. Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. Set the technical direction for enterprise-wide AI architecture - unifying tooling, observability, and deployment standards across teams Own the design and integration of model routing, caching, and orchestration systems that support hybrid and multi-model workloads Champion responsible AI principles across all system design, embedding transparency, reproducibility, and fairness-by-design Drive internal education, mentorship, and dissemination of best practices through architecture councils and AI guilds The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enables you to see and exploit optimization opportunities that others miss You are a resilient trailblazer who can forge new paths to achieve business goals when the route is unknown Capital One is open to hiring a Remote Employee for this opportunity. Basic Qualifications: Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies At least 8 years of experience programming with Python, Go, Scala, CUDA, or Java Preferred Qualifications: Experience designing AI systems with tradeoff decisions around cost, latency, throughput and accuracy 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Experience in building agentic AI systems and agentic workflows Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Proven track record of defining and operationalizing enterprise AI architecture standards, including data pipeline governance, observability and evaluation frameworks Experience leading federated or multi-cloud AI strategies to maximize resilience, compliance and compute efficiency Demonstrated success influencing research-to-production promotion processes - guiding model handoff, evaluation and productization Experience defining north-star metrics for AI systems that balance business value, innovation velocity, cost, and ethical responsibility Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) Capital One will consider sponsoring a new qualified applicant for employment authorization for this position 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. Remote (Regardless of Location): $244,700 - $279,200 for Staff AI Engineer Cambridge, MA: $269,100 - $307,200 for Staff AI Engineer McLean, VA: $269,100 - $307,200 for Staff AI Engineer New York, NY: $293,600 - $335,100 for Staff AI Engineer San Francisco, CA: $293,600 - $335,100 for Staff AI Engineer San Jose, CA: $293,600 - $335,100 for Staff AI Engineer 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 . click apply for full job details
09/30/2026
Full time
Staff AI Engineer - Enterprise Analysis Platform (Remote Eligible) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is currently working on building a new AI native platform to empower our broader business community to execute AI assisted analysis across our many lines of business. We are building an AI native, at-scale (15,000+ users at destination), enterprise platform intertwining deterministic code with non-deterministic reasoning systems as well as both frontier and home grown AI models, to transform how our company works in the data analysis space. What You'll Do: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more. Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. Set the technical direction for enterprise-wide AI architecture - unifying tooling, observability, and deployment standards across teams Own the design and integration of model routing, caching, and orchestration systems that support hybrid and multi-model workloads Champion responsible AI principles across all system design, embedding transparency, reproducibility, and fairness-by-design Drive internal education, mentorship, and dissemination of best practices through architecture councils and AI guilds The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enables you to see and exploit optimization opportunities that others miss You are a resilient trailblazer who can forge new paths to achieve business goals when the route is unknown Capital One is open to hiring a Remote Employee for this opportunity. Basic Qualifications: Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies At least 8 years of experience programming with Python, Go, Scala, CUDA, or Java Preferred Qualifications: Experience designing AI systems with tradeoff decisions around cost, latency, throughput and accuracy 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Experience in building agentic AI systems and agentic workflows Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Proven track record of defining and operationalizing enterprise AI architecture standards, including data pipeline governance, observability and evaluation frameworks Experience leading federated or multi-cloud AI strategies to maximize resilience, compliance and compute efficiency Demonstrated success influencing research-to-production promotion processes - guiding model handoff, evaluation and productization Experience defining north-star metrics for AI systems that balance business value, innovation velocity, cost, and ethical responsibility Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) Capital One will consider sponsoring a new qualified applicant for employment authorization for this position 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. Remote (Regardless of Location): $244,700 - $279,200 for Staff AI Engineer Cambridge, MA: $269,100 - $307,200 for Staff AI Engineer McLean, VA: $269,100 - $307,200 for Staff AI Engineer New York, NY: $293,600 - $335,100 for Staff AI Engineer San Francisco, CA: $293,600 - $335,100 for Staff AI Engineer San Jose, CA: $293,600 - $335,100 for Staff AI Engineer 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 . click apply for full job details
Staff Machine Learning Engineer 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. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent 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 8 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 5 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 architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ 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) 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. 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: $269,100 - $307,200 for Staff Machine Learning Engineer Plano, TX: $244,700 - $279,200 for Staff Machine Learning Engineer 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).
09/30/2026
Full time
Staff Machine Learning Engineer 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. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent 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 8 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 5 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 architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ 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) 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. 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: $269,100 - $307,200 for Staff Machine Learning Engineer Plano, TX: $244,700 - $279,200 for Staff Machine Learning Engineer 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).
