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Job Description Job Description Description: RISE POWER SYSTEMS Location: Richmond, VA (Primary) Raleigh, NC (Alternate) Job Type: Full-Time Hybrid, with travel as business needs require Salary Range: $90,000-$110,000 annually Power the Future with Rise Power Systems! Rise Power Systems (RPS), a division of SMT, Inc., is seeking a motivated and detail-oriented Applications Engineer I to join our growing team. This is an exciting opportunity to build your career in the rapidly expanding mission-critical power industry while supporting innovative electrical power distribution solutions for data centers and other critical infrastructure applications. In this role, you'll work alongside experienced engineering, sales, and operations professionals to develop technical solutions that meet customer needs. You'll gain hands-on experience reviewing project specifications, configuring equipment, preparing technical quotations, and supporting projects from initial customer inquiry through engineering handoff. We're looking for someone with a solid technical foundation, strong problem-solving abilities, and a desire to learn and grow. If you have experience in electrical manufacturing, engineering support, estimating, quality, or related technical fields, this could be an excellent next step in your career. What You'll Do As an Applications Engineer I, you'll play an important role in helping customers identify the right power distribution solutions for their applications. Review Customer Requirements: Evaluate requests for quotation (RFQs), technical specifications, electrical one-line diagrams, drawings, and project requirements for data center and critical power applications. Support Product Selection: Assist with selecting and configuring remote power panels (RPPs), high-density remote power panels (HDRPPs), power distribution units (PDUs), switchboards, panelboards, transformers, breakers, monitoring systems, and related equipment. Prepare Technical Quotations: Develop accurate, comprehensive quote packages that include equipment configurations, options, assumptions, clarifications, exclusions, and required commercial information. Identify Technical Challenges: Recognize missing information, specification gaps, potential risks, and requirements that need clarification before quotation or execution. Collaborate with Sales and Customers: Provide technical support during customer discussions and help identify product configurations and solutions that meet project requirements. Coordinate with Suppliers: Work with vendors to obtain pricing, lead times, technical specifications, product documentation, and application support. Partner Across Departments: Collaborate with Engineering, Purchasing, Operations, Project Management, and Sales to ensure accurate project handoffs and successful execution. Drive Continuous Improvement: Help improve applications engineering tools, processes, and documentation to support efficiency, accuracy, and customer satisfaction. Requirements: What We're Looking For Required Qualifications At least 2 years of relevant experience in electrical manufacturing, quality, service, applications engineering, estimating, product support, sales engineering, or a related technical field. Experience with data center equipment, mission-critical applications, electrical manufacturing, or low-voltage power distribution systems. Familiarity with electrical distribution equipment such as RPPs, PDUs, switchboards, panelboards, breakers, transformers, and monitoring systems. Ability to read and interpret electrical one-line diagrams, technical drawings, customer specifications, and equipment documentation. Working knowledge of electrical distribution principles, including voltage, current, breaker selection, short-circuit ratings, grounding, and monitoring. Strong analytical, organizational, communication, and problem-solving skills. Ability to collaborate effectively with technical teams, sales professionals, suppliers, and customers. A willingness to learn, receive technical guidance, take ownership of assignments, and progressively develop independent applications engineering capabilities. Preferred Qualifications Bachelor's degree in Electrical Engineering, Electrical Engineering Technology, or a related technical discipline. Equivalent relevant industry experience may be considered. Experience supporting hyperscale, colocation, or enterprise data center customers. Familiarity with UL 891, UL 67, or UL 508A standards. Experience using product configurators, costing tools, or configure-price-quote (CPQ) systems. Knowledge of Modbus, Ethernet communications, branch circuit monitoring, power meters, SCADA, or similar monitoring technologies. Why Join Rise Power Systems? At Rise Power Systems, you'll have the opportunity to contribute to an industry that plays a vital role in supporting today's digital infrastructure. As demand for reliable, high-performance power solutions continues to grow, so does the opportunity to develop your technical expertise and advance your career. This position offers: Career Development: Build your technical knowledge and grow toward independently managing customer applications and opportunities. Meaningful Work: Support power distribution solutions for data centers and other mission-critical facilities. Collaborative Environment: Work with experienced professionals across engineering, manufacturing, sales, and operations. Growth Opportunities: Join a growing organization focused on innovation, quality, and delivering reliable power infrastructure. Benefits Rise Power Systems offers a competitive benefits package designed to support employees and their families, including: Medical, dental, and vision insurance Life insurance and short-term disability coverage Supplemental insurance options Pet insurance 401(k) retirement plan with company match Paid time off (PTO) and paid holidays Ready to Build Your Future with Us? If you're a technically minded professional who enjoys solving problems, collaborating with others, and developing practical solutions for complex electrical applications, we'd love to hear from you. Apply today and explore your future with Rise Power Systems! Rise Power Systems is an Equal Opportunity Employer. PI7f67e8014ccc-0502

Job Description Job Description Description: Position Summary Richmond, VA (primary) Hybrid role with travel as needed based on business needs. Rise Power Systems is seeking a Senior Applications Engineer to support customer opportunities and technical execution for mission-critical power distribution products. This role is intended for an experienced engineer who can independently own RFQ and specification review, product selection and configuration, technical quote development, customer and vendor coordination, and engineering handoff with limited oversight. Benefits: Rise Power Systems offers a generous benefits package to include the following: Medical, Dental, Vision, Life Insurance, Short Term Disability, Supplemental Insurance Options, Pet Insurance 401k with company match PTO and Paid Holidays Key Responsibilities Independently review customer RFQs, specifications, one-line diagrams, drawings, and technical requirements for data center and critical power applications. Select and configure appropriate low-voltage power distribution solutions, including RPPs, HDRPPs, PDUs, switchboards, panelboards, transformers, breakers, monitoring systems, and related equipment. Develop complete technical quote packages, including configurations, options, clarifications, exclusions, assumptions, and required commercial inputs. Identify specification gaps, technical risks, missing information, and customer requirements that require clarification before quotation or execution. Provide technical guidance to customers and Sales during opportunity development, including product application, configuration, and solution discussions. Coordinate with vendors and suppliers to obtain pricing, lead times, technical data, documentation, and application support. Coordinate with Engineering, Purchasing, Operations, Project Management, and Sales to ensure accurate technical handoff and execution of customer requirements. Improve application engineering tools and processes, including costing tools, quote templates, standard options, configuration tools, documentation, and best practices. Requirements: Required Qualifications 5+ years of relevant experience in applications engineering, sales engineering, product engineering, estimating, or technical proposal development; exceptionally strong direct industry experience may be considered with somewhat less overall tenure. Direct experience with data center, mission-critical, electrical manufacturing, or low-voltage power distribution equipment. Working knowledge of RPPs, HDRPPs, PDUs, switchboards, panelboards, breakers, transformers, monitoring systems, and related electrical infrastructure. Demonstrated ability to read and interpret customer specifications, one-line diagrams, electrical drawings, and technical requirements. Strong understanding of electrical power distribution concepts, including voltage, current, breaker selection, short-circuit ratings, grounding, monitoring, and coordination considerations. Experience independently developing technical quotations, proposal packages, customer submittals, or engineered-to-order product solutions. Strong customer-facing and cross-functional communication skills, with the judgment to work effectively with Sales, Engineering, Purchasing, Operations, vendors, and customers. Ability to work independently in a fast-moving environment with limited supervision, strong attention to detail, and clear ownership and accountability. Preferred Experience Experience with hyperscale, colocation, or enterprise data center customers; UL 891 switchboards, UL 67 panelboards, UL 508A controls, or similar standards; product configurators, costing or CPQ tools; Modbus, Ethernet, branch circuit monitoring, power meters, SCADA integration, or remote monitoring; and SolidWorks or similar 3D CAD software is a plus. A bachelor's degree in Electrical Engineering, Electrical Engineering Technology, or a related technical field is preferred; equivalent industry experience may be considered PI9f33c19c5fae-1861

Director, 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 a hands-on people leader for 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 people leader, 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, or Java Evangelize best practices in all aspects of the engineering and modeling lifecycles Recruit, nurture, and retain top engineering talent Serve as a force-multiplier for the team, balancing deep, hands-on technical contribution and innovation with mentoring and elevating the skills of peers and junior engineers Recruit, nurture, and retain top engineering talent Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the engineering community 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 3 years of people leadership experience 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 5+ years of experience managing and leading an engineering team 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 hiring and developing high-performing ML engineers with an inspiring leadership style Highly developed interpersonal, presentation, and communications skills Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents 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 Director, Machine Learning Engineer New York, NY: $293,600 - $335,100 for Director, Machine Learning Engineer San Francisco, CA: $293,600 - $335,100 for Director, Machine Learning Engineer San Jose, CA: $293,600 - $335,100 for Director, 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).

Director, 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 a hands-on people leader for 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 people leader, 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, or Java Evangelize best practices in all aspects of the engineering and modeling lifecycles Recruit, nurture, and retain top engineering talent Serve as a force-multiplier for the team, balancing deep, hands-on technical contribution and innovation with mentoring and elevating the skills of peers and junior engineers Recruit, nurture, and retain top engineering talent Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the engineering community 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 3 years of people leadership experience 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 5+ years of experience managing and leading an engineering team 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 hiring and developing high-performing ML engineers with an inspiring leadership style Highly developed interpersonal, presentation, and communications skills Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents 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 Director, Machine Learning Engineer New York, NY: $293,600 - $335,100 for Director, Machine Learning Engineer San Francisco, CA: $293,600 - $335,100 for Director, Machine Learning Engineer San Jose, CA: $293,600 - $335,100 for Director, 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).

Description: Position Summary Richmond, VA (primary) Hybrid role with travel as needed based on business needs. Rise Power Systems is seeking a Senior Applications Engineer to support customer opportunities and technical execution for mission-critical power distribution products. This role is intended for an experienced engineer who can independently own RFQ and specification review, product selection and configuration, technical quote development, customer and vendor coordination, and engineering handoff with limited oversight. Benefits: Rise Power Systems offers a generous benefits package to include the following: Medical, Dental, Vision, Life Insurance, Short Term Disability, Supplemental Insurance Options, Pet Insurance 401k with company match PTO and Paid Holidays Key Responsibilities Independently review customer RFQs, specifications, one-line diagrams, drawings, and technical requirements for data center and critical power applications. Select and configure appropriate low-voltage power distribution solutions, including RPPs, HDRPPs, PDUs, switchboards, panelboards, transformers, breakers, monitoring systems, and related equipment. Develop complete technical quote packages, including configurations, options, clarifications, exclusions, assumptions, and required commercial inputs. Identify specification gaps, technical risks, missing information, and customer requirements that require clarification before quotation or execution. Provide technical guidance to customers and Sales during opportunity development, including product application, configuration, and solution discussions. Coordinate with vendors and suppliers to obtain pricing, lead times, technical data, documentation, and application support. Coordinate with Engineering, Purchasing, Operations, Project Management, and Sales to ensure accurate technical handoff and execution of customer requirements. Improve application engineering tools and processes, including costing tools, quote templates, standard options, configuration tools, documentation, and best practices. Requirements: Required Qualifications 5+ years of relevant experience in applications engineering, sales engineering, product engineering, estimating, or technical proposal development; exceptionally strong direct industry experience may be considered with somewhat less overall tenure. Direct experience with data center, mission-critical, electrical manufacturing, or low-voltage power distribution equipment. Working knowledge of RPPs, HDRPPs, PDUs, switchboards, panelboards, breakers, transformers, monitoring systems, and related electrical infrastructure. Demonstrated ability to read and interpret customer specifications, one-line diagrams, electrical drawings, and technical requirements. Strong understanding of electrical power distribution concepts, including voltage, current, breaker selection, short-circuit ratings, grounding, monitoring, and coordination considerations. Experience independently developing technical quotations, proposal packages, customer submittals, or engineered-to-order product solutions. Strong customer-facing and cross-functional communication skills, with the judgment to work effectively with Sales, Engineering, Purchasing, Operations, vendors, and customers. Ability to work independently in a fast-moving environment with limited supervision, strong attention to detail, and clear ownership and accountability. Preferred Experience Experience with hyperscale, colocation, or enterprise data center customers; UL 891 switchboards, UL 67 panelboards, UL 508A controls, or similar standards; product configurators, costing or CPQ tools; Modbus, Ethernet, branch circuit monitoring, power meters, SCADA integration, or remote monitoring; and SolidWorks or similar 3D CAD software is a plus. A bachelor's degree in Electrical Engineering, Electrical Engineering Technology, or a related technical field is preferred; equivalent industry experience may be considered PI64f3b48f554b-1861

Description: We are seeking a motivated Applications Engineer to support our Skidded Solutions (liquid-filled medium voltage transformers, switchgear cabinets, inverters) in the solar and energy storage industries. The Applications Engineer will be responsible for interfacing with Customers (current and potential) in an effort to achieve revenue growth and customer acquisition objectives set forth by the Company. The Applications Engineer will review customer specifications, assist in determining and understanding client equipment needs, generate quotes, and engage in follow up conversations with the customer regarding open opportunities. The ideal candidate will possess strong technical expertise in mechanical design and practical integration, focusing on the design, optimization, and mechanical integration of structural skids with transformers and switchgear. The candidate will collaborate with cross-functional teams to develop solutions that meet customer requirements, industry standards, and regulatory compliance. This role involves working closely with sales, product management, design engineering, and customers to ensure optimal performance, reliability, and cost-effectiveness. Key Responsibilities : Technical Expertise: • Serve as a technical resource on the mechanical integration and optimization of skidded liquid-filled medium voltage transformers, switchgear cabinets and inverters for solar and energy storage applications. • Provide technical guidance on product selection, specifications, and customization to meet customer requirements. • Analyze technical problems and recommend solutions. Customer Support: • Work directly with customers to understand their needs, address inquiries, and recommend appropriate integrated design solutions. • Prepare and deliver technical presentations and proposals, including customer-facing quotes in support of the sales process. • Support sales with technical training and customer visits. Application Engineering: • Interpret customer specifications and industry standards to create accurate design requirements. • Collaborate with design engineering and manufacturing teams to ensure products are readily manufacturable and meet performance, reliability, and cost targets. • Develop and maintain technical documentation, including drawings, manuals, and data sheets. • Identify value engineering opportunities within integrated designs to optimize cost and capability in support of customer needs Project Support: • Assist in managing multiple projects, ensuring timely delivery of proposals, designs, and support. • Coordinate with supply chain and production teams to address lead times and resource planning. Industry and Standards Compliance: • Stay current with industry standards, codes, and regulations such as IEEE, IEC, UL and ASCE. • Ensure products and applications comply with environmental and safety regulations. Qualifications : • Bachelor's degree in Mechanical Engineering or a related field. • Minimum of 2-5 years of professional experience in application or mechanical design engineering, preferably with mechanical integration of electrical equipment • Experience in the solar or energy storage industries is a plus. • Solid knowledge of mechanical design principles and design for manufacturability • Familiarity with mechanical systems and pre-configured/skidded electromechanical assemblies • Proficiency in Mechanical design software and tools - SolidWorks and/or AutoCAD; FEA modeling a plus Electrical, Control systems and/or data acquisition & field monitoring proficiency a plus Soft Skills: • Strong communication and presentation skills. • Excellent problem-solving abilities and attention to detail. • Ability to work collaboratively in a cross-functional team environment. Certifications: • Professional Engineer (PE) license is a plus. Disclaimer: The statements above are intended to describe the general nature and level of work being performed. They are not an exhaustive list of all responsibilities, duties, or skills required. Forgent Power reserves the right to modify, interpret, or apply this job description as needed. Equal Employment Opportunity Statement: Forgent Power is an equal opportunity employer. We are committed to creating an inclusive environment for all employees. Employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other protected class. Requirements: PI57640b6d30fa-9955

Description: RISE POWER SYSTEMS Location: Richmond, VA (Primary) Raleigh, NC (Alternate) Job Type: Full-Time Hybrid, with travel as business needs require Salary Range: $90,000-$110,000 annually Power the Future with Rise Power Systems! Rise Power Systems (RPS), a division of SMT, Inc., is seeking a motivated and detail-oriented Applications Engineer I to join our growing team. This is an exciting opportunity to build your career in the rapidly expanding mission-critical power industry while supporting innovative electrical power distribution solutions for data centers and other critical infrastructure applications. In this role, you'll work alongside experienced engineering, sales, and operations professionals to develop technical solutions that meet customer needs. You'll gain hands-on experience reviewing project specifications, configuring equipment, preparing technical quotations, and supporting projects from initial customer inquiry through engineering handoff. We're looking for someone with a solid technical foundation, strong problem-solving abilities, and a desire to learn and grow. If you have experience in electrical manufacturing, engineering support, estimating, quality, or related technical fields, this could be an excellent next step in your career. What You'll Do As an Applications Engineer I, you'll play an important role in helping customers identify the right power distribution solutions for their applications. Review Customer Requirements: Evaluate requests for quotation (RFQs), technical specifications, electrical one-line diagrams, drawings, and project requirements for data center and critical power applications. Support Product Selection: Assist with selecting and configuring remote power panels (RPPs), high-density remote power panels (HDRPPs), power distribution units (PDUs), switchboards, panelboards, transformers, breakers, monitoring systems, and related equipment. Prepare Technical Quotations: Develop accurate, comprehensive quote packages that include equipment configurations, options, assumptions, clarifications, exclusions, and required commercial information. Identify Technical Challenges: Recognize missing information, specification gaps, potential risks, and requirements that need clarification before quotation or execution. Collaborate with Sales and Customers: Provide technical support during customer discussions and help identify product configurations and solutions that meet project requirements. Coordinate with Suppliers: Work with vendors to obtain pricing, lead times, technical specifications, product documentation, and application support. Partner Across Departments: Collaborate with Engineering, Purchasing, Operations, Project Management, and Sales to ensure accurate project handoffs and successful execution. Drive Continuous Improvement: Help improve applications engineering tools, processes, and documentation to support efficiency, accuracy, and customer satisfaction. Requirements: What We're Looking For Required Qualifications At least 2 years of relevant experience in electrical manufacturing, quality, service, applications engineering, estimating, product support, sales engineering, or a related technical field. Experience with data center equipment, mission-critical applications, electrical manufacturing, or low-voltage power distribution systems. Familiarity with electrical distribution equipment such as RPPs, PDUs, switchboards, panelboards, breakers, transformers, and monitoring systems. Ability to read and interpret electrical one-line diagrams, technical drawings, customer specifications, and equipment documentation. Working knowledge of electrical distribution principles, including voltage, current, breaker selection, short-circuit ratings, grounding, and monitoring. Strong analytical, organizational, communication, and problem-solving skills. Ability to collaborate effectively with technical teams, sales professionals, suppliers, and customers. A willingness to learn, receive technical guidance, take ownership of assignments, and progressively develop independent applications engineering capabilities. Preferred Qualifications Bachelor's degree in Electrical Engineering, Electrical Engineering Technology, or a related technical discipline. Equivalent relevant industry experience may be considered. Experience supporting hyperscale, colocation, or enterprise data center customers. Familiarity with UL 891, UL 67, or UL 508A standards. Experience using product configurators, costing tools, or configure-price-quote (CPQ) systems. Knowledge of Modbus, Ethernet communications, branch circuit monitoring, power meters, SCADA, or similar monitoring technologies. Why Join Rise Power Systems? At Rise Power Systems, you'll have the opportunity to contribute to an industry that plays a vital role in supporting today's digital infrastructure. As demand for reliable, high-performance power solutions continues to grow, so does the opportunity to develop your technical expertise and advance your career. This position offers: Career Development: Build your technical knowledge and grow toward independently managing customer applications and opportunities. Meaningful Work: Support power distribution solutions for data centers and other mission-critical facilities. Collaborative Environment: Work with experienced professionals across engineering, manufacturing, sales, and operations. Growth Opportunities: Join a growing organization focused on innovation, quality, and delivering reliable power infrastructure. Benefits Rise Power Systems offers a competitive benefits package designed to support employees and their families, including: Medical, dental, and vision insurance Life insurance and short-term disability coverage Supplemental insurance options Pet insurance 401(k) retirement plan with company match Paid time off (PTO) and paid holidays Ready to Build Your Future with Us? If you're a technically minded professional who enjoys solving problems, collaborating with others, and developing practical solutions for complex electrical applications, we'd love to hear from you. Apply today and explore your future with Rise Power Systems! Rise Power Systems is an Equal Opportunity Employer. PI5ce634342eec-0502

Description: Position Summary Richmond, VA (primary) Hybrid role with travel as needed based on business needs. Rise Power Systems is seeking a Senior Applications Engineer to support customer opportunities and technical execution for mission-critical power distribution products. This role is intended for an experienced engineer who can independently own RFQ and specification review, product selection and configuration, technical quote development, customer and vendor coordination, and engineering handoff with limited oversight. Benefits: Rise Power Systems offers a generous benefits package to include the following: Medical, Dental, Vision, Life Insurance, Short Term Disability, Supplemental Insurance Options, Pet Insurance 401k with company match PTO and Paid Holidays Key Responsibilities Independently review customer RFQs, specifications, one-line diagrams, drawings, and technical requirements for data center and critical power applications. Select and configure appropriate low-voltage power distribution solutions, including RPPs, HDRPPs, PDUs, switchboards, panelboards, transformers, breakers, monitoring systems, and related equipment. Develop complete technical quote packages, including configurations, options, clarifications, exclusions, assumptions, and required commercial inputs. Identify specification gaps, technical risks, missing information, and customer requirements that require clarification before quotation or execution. Provide technical guidance to customers and Sales during opportunity development, including product application, configuration, and solution discussions. Coordinate with vendors and suppliers to obtain pricing, lead times, technical data, documentation, and application support. Coordinate with Engineering, Purchasing, Operations, Project Management, and Sales to ensure accurate technical handoff and execution of customer requirements. Improve application engineering tools and processes, including costing tools, quote templates, standard options, configuration tools, documentation, and best practices. Requirements: Required Qualifications 5+ years of relevant experience in applications engineering, sales engineering, product engineering, estimating, or technical proposal development; exceptionally strong direct industry experience may be considered with somewhat less overall tenure. Direct experience with data center, mission-critical, electrical manufacturing, or low-voltage power distribution equipment. Working knowledge of RPPs, HDRPPs, PDUs, switchboards, panelboards, breakers, transformers, monitoring systems, and related electrical infrastructure. Demonstrated ability to read and interpret customer specifications, one-line diagrams, electrical drawings, and technical requirements. Strong understanding of electrical power distribution concepts, including voltage, current, breaker selection, short-circuit ratings, grounding, monitoring, and coordination considerations. Experience independently developing technical quotations, proposal packages, customer submittals, or engineered-to-order product solutions. Strong customer-facing and cross-functional communication skills, with the judgment to work effectively with Sales, Engineering, Purchasing, Operations, vendors, and customers. Ability to work independently in a fast-moving environment with limited supervision, strong attention to detail, and clear ownership and accountability. Preferred Experience Experience with hyperscale, colocation, or enterprise data center customers; UL 891 switchboards, UL 67 panelboards, UL 508A controls, or similar standards; product configurators, costing or CPQ tools; Modbus, Ethernet, branch circuit monitoring, power meters, SCADA integration, or remote monitoring; and SolidWorks or similar 3D CAD software is a plus. A bachelor's degree in Electrical Engineering, Electrical Engineering Technology, or a related technical field is preferred; equivalent industry experience may be considered PI4adaf-1861

Description: RISE POWER SYSTEMS Location: Richmond, VA (Primary) Raleigh, NC (Alternate) Job Type: Full-Time Hybrid, with travel as business needs require Salary Range: $90,000-$110,000 annually Power the Future with Rise Power Systems! Rise Power Systems (RPS), a division of SMT, Inc., is seeking a motivated and detail-oriented Applications Engineer I to join our growing team. This is an exciting opportunity to build your career in the rapidly expanding mission-critical power industry while supporting innovative electrical power distribution solutions for data centers and other critical infrastructure applications. In this role, you'll work alongside experienced engineering, sales, and operations professionals to develop technical solutions that meet customer needs. You'll gain hands-on experience reviewing project specifications, configuring equipment, preparing technical quotations, and supporting projects from initial customer inquiry through engineering handoff. We're looking for someone with a solid technical foundation, strong problem-solving abilities, and a desire to learn and grow. If you have experience in electrical manufacturing, engineering support, estimating, quality, or related technical fields, this could be an excellent next step in your career. What You'll Do As an Applications Engineer I, you'll play an important role in helping customers identify the right power distribution solutions for their applications. Review Customer Requirements: Evaluate requests for quotation (RFQs), technical specifications, electrical one-line diagrams, drawings, and project requirements for data center and critical power applications. Support Product Selection: Assist with selecting and configuring remote power panels (RPPs), high-density remote power panels (HDRPPs), power distribution units (PDUs), switchboards, panelboards, transformers, breakers, monitoring systems, and related equipment. Prepare Technical Quotations: Develop accurate, comprehensive quote packages that include equipment configurations, options, assumptions, clarifications, exclusions, and required commercial information. Identify Technical Challenges: Recognize missing information, specification gaps, potential risks, and requirements that need clarification before quotation or execution. Collaborate with Sales and Customers: Provide technical support during customer discussions and help identify product configurations and solutions that meet project requirements. Coordinate with Suppliers: Work with vendors to obtain pricing, lead times, technical specifications, product documentation, and application support. Partner Across Departments: Collaborate with Engineering, Purchasing, Operations, Project Management, and Sales to ensure accurate project handoffs and successful execution. Drive Continuous Improvement: Help improve applications engineering tools, processes, and documentation to support efficiency, accuracy, and customer satisfaction. Requirements: What We're Looking For Required Qualifications At least 2 years of relevant experience in electrical manufacturing, quality, service, applications engineering, estimating, product support, sales engineering, or a related technical field. Experience with data center equipment, mission-critical applications, electrical manufacturing, or low-voltage power distribution systems. Familiarity with electrical distribution equipment such as RPPs, PDUs, switchboards, panelboards, breakers, transformers, and monitoring systems. Ability to read and interpret electrical one-line diagrams, technical drawings, customer specifications, and equipment documentation. Working knowledge of electrical distribution principles, including voltage, current, breaker selection, short-circuit ratings, grounding, and monitoring. Strong analytical, organizational, communication, and problem-solving skills. Ability to collaborate effectively with technical teams, sales professionals, suppliers, and customers. A willingness to learn, receive technical guidance, take ownership of assignments, and progressively develop independent applications engineering capabilities. Preferred Qualifications Bachelor's degree in Electrical Engineering, Electrical Engineering Technology, or a related technical discipline. Equivalent relevant industry experience may be considered. Experience supporting hyperscale, colocation, or enterprise data center customers. Familiarity with UL 891, UL 67, or UL 508A standards. Experience using product configurators, costing tools, or configure-price-quote (CPQ) systems. Knowledge of Modbus, Ethernet communications, branch circuit monitoring, power meters, SCADA, or similar monitoring technologies. Why Join Rise Power Systems? At Rise Power Systems, you'll have the opportunity to contribute to an industry that plays a vital role in supporting today's digital infrastructure. As demand for reliable, high-performance power solutions continues to grow, so does the opportunity to develop your technical expertise and advance your career. This position offers: Career Development: Build your technical knowledge and grow toward independently managing customer applications and opportunities. Meaningful Work: Support power distribution solutions for data centers and other mission-critical facilities. Collaborative Environment: Work with experienced professionals across engineering, manufacturing, sales, and operations. Growth Opportunities: Join a growing organization focused on innovation, quality, and delivering reliable power infrastructure. Benefits Rise Power Systems offers a competitive benefits package designed to support employees and their families, including: Medical, dental, and vision insurance Life insurance and short-term disability coverage Supplemental insurance options Pet insurance 401(k) retirement plan with company match Paid time off (PTO) and paid holidays Ready to Build Your Future with Us? If you're a technically minded professional who enjoys solving problems, collaborating with others, and developing practical solutions for complex electrical applications, we'd love to hear from you. Apply today and explore your future with Rise Power Systems! Rise Power Systems is an Equal Opportunity Employer. PI766da2405c11-0502

Intro Are you ready to explore a world of possibilities, both at work and during your time off? Join our American Airlines family, and you'll travel the world, grow your expertise and become the best version of you. As you embark on a new journey, you'll tackle challenges with flexibility and grace, learning new skills and advancing your career while having the time of your life. Feel free to enrich both your personal and work life and hop on board! Why you'll love this job As one diverse, high-performing team dedicated to technical excellence, you will focus relentlessly on delivering unrivaled digital products that drive a more reliable and profitable airline. AI domain encompasses disciplines and job families dedicated to the creation, implementation, and management of artificial intelligence technologies. This domain includes roles focused on the research, design, engineering, application, and governance of AI systems. Within the AI domain, professionals work on developing intelligent algorithms and data-driven solutions to solve complex problems and improve decision-making processes. The scope of AI covers various subfields, including natural language processing (NLP), reinforcement learning, and generative AI. What you'll do As noted above, this list is intended to reflect the current job but there may be additional essential functions (and certainly non-essential job functions) that are not referenced. Management will modify the job or require other tasks be performed whenever it is deemed appropriate to do so, observing, of course, any legal obligations including any collective bargaining obligations. Collaborate with leaders, business analysts, project managers, IT architects, technical leads and other developers, along with internal customers, to understand requirements and develop needs according to business requirements for AI solutions Maintain and enhance existing enterprise services, applications, and platforms using domain driven design and test-driven development Troubleshoot and debug complex issues; identify and implement solutions Create detailed project specifications, requirements, and estimates Research and implement new AI technologies to enhance current processes, security, and performance Work closely with software architects and technical leads to ensure decisions meet long-term enterprise growth needs Work on pioneering projects that harness the power of artificial intelligence to solve complex problems and create significant business value. Responsible for participating in all phases of the development process, following agile processes and test-driven development, and using object-oriented development tools to analyze, model, design, construct and test reusable objects. Partner with a diverse team of experts, leveraging cutting-edge technologies to build scalable and impactful AI solutions. All you'll need for success Minimum Qualifications- Education & Prior Job Experience Bachelor's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training 3+ years of full Software Development Life Cycle (SDLC) experience designing, developing, and implementing large-scale applications in hosted production environments 3+ years of professional, design, and open-source experience Preferred Qualifications- Education & Prior Job Experience Master's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training 5 years of full Software Development Life Cycle (SDLC) experience Prior experience designing and building agentic systems Prior experience developing and implementing AI solutions using Large-Language Models (LLMs) Airline or transport industry experience is a plus Skills, Licenses & Certifications Practical understanding of transformer architectures and how modern LLMs work Experience building agentic orchestration using frameworks such as Semantic Kernel, LangChain/LangGraph, Microsoft Copilot Studio, OpenAI/Anthropic tool-calling patterns, or equivalent internal frameworks Proficiency in prompt engineering: systematic prompt design, few-shot learning, chain-of-thought, and prompt optimization for foundation models Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate, Chroma) and RAG architecture patterns Experience with LLM evaluation methods, including LLM-as-a-judge, automated benchmarking, and regression testing for model outputs Proficiency in post-deployment model maintenance, including observability, monitoring, cost optimization, and drift detection Experience developing and implementing governance frameworks and controls for responsible AI Familiarity with AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code) is a plus Knowledge of experimentation and measurement: A/B testing, KPI instrumentation, and operational analytics Strong proficiency in Python; working knowledge of Java or another enterprise language Proficiency in object-oriented design techniques and principles Experience with cloud platforms (Azure, AWS, or GCP) and cloud-native development patterns Experience with CI/CD pipelines and DevOps toolchain: Git, GitHub, Jenkins, ADO Pipelines, or equivalent Experience with RESTful API design and integration patterns (JSON, microservices) Experience in Agile methodologies, such as SCRUM What you'll get Feel free to take advantage of all that American Airlines has to offer: Travel Perks: Ready to explore the world? You, your family and your friends can reach 365 destinations on more than 6,800 daily flights across our global network. Health Benefits: On day one, you'll have access to your health, dental, prescription and vision benefits to help you stay well. And that's just the start, we also offer virtual doctor visits, flexible spending accounts and more. Wellness Programs: We want you to be the best version of yourself - that's why our wellness programs provide you with all the right tools, resources and support you need. 401(k) Program: Available upon hire and, depending on the workgroup, employer contributions to your 401(k) program are available after one year. Additional Benefits: Other great benefits include our Member Assistance Program, pet insurance and discounts on hotels, cars, cruises and more Feel free to be yourself at American From the team members we hire to the customers we serve, inclusion and diversity are the foundation of the dynamic workforce at American Airlines. Our 20+ Team Member Resource Groups are focused on connecting our team members to our customers, suppliers, communities and shareholders, helping team members reach their full potential and creating an inclusive work environment to meet and exceed the needs of our diverse world. Are you ready to feel a tremendous sense of pride and satisfaction as you do your part to keep the largest airline in the world running smoothly as we care for people on life's journey? Feel free to be yourself at American.

Machine Learning Engineer 5, Senior Manager - Intelligent Foundations and Experiences (IFX) 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: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 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 4 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 +2 years experience building production RAG pipelines (chunking, embeddings, vector indexing, graph RAG, or fine-tuning). +2 years experience leveraging Agentic patterns (Tool Calling, ReAct loops, Plan-and-Execute, Multi-Agent Collaboration). +2 years with LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) and model APIs (OpenAI, Anthropic, Bedrock, Ollama, Hugging Face). +2 year experience in building agentic AI systems and agentic workflows +2 year 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 +2 years Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 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) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences 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: $229,900 - $262,400 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 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).

Machine Learning Engineer 4 - Intelligent Foundations and Experiences (IFX) 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: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 5 (Senior Manager, IC) In Risk Tech, we provide the foundation for Capital One to thrive in an uncertain world. Our engaged, empowered, and intelligent people produce outstanding products, working toward the common goal of transforming risk management with technology. We build data-driven tools that use machine learning to prevent risks & automatically detect issues before they impact our customers, our business, or our communities. In this role at Risk Tech, you will work with our GRC team and partners across the company to build and deploy proprietary solutions for Risk management that are powered by state-of-the-art AI technology. Our products, enhanced with the transformative power of AI, are central to our business and deliver tremendous customer value. 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: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 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/ML data At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 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) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences 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: $229,900 - $262,400 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 - Machine Learning Experiences (MLX) 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: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ) The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Sales Territory: $179,400 - $204,700 for Machine Learning Engineer 4 Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 5 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: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 6 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 4 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 optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 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) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences 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: $229,900 - $262,400 for Machine Learning Engineer 5 New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5 Plano, TX: $209,000 - $238,500 for Machine Learning Engineer 5 Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5 San Francisco, CA: $250,800 - $286,200 for Machine Learning Engineer 5 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).

Senior Manager, Machine Learning Engineer (People Leader) 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: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale. Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Serve as a force-multiplier for the team, balancing deep, hands-on technical contribution and innovation with mentoring and elevating the skills of peers and junior engineers Recruit, nurture, and retain top engineering talent Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, and mentoring other members of the engineering community 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 6 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 4 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 At least 1 year of people leadership experience Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience managing and leading an ML engineering team 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans 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) 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents 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: $229,900 - $262,400 for Sr. Manager, Machine Learning Engineer New York, NY: $250,800 - $286,200 for Sr. Manager, Machine Learning Engineer Plano, TX: $209,000 - $238,500 for Sr. Manager, Machine Learning Engineer San Francisco, CA: $250,800 - $286,200 for Sr. Manager, 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).

Machine Learning Engineer 4 (Manager, IC) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that streamline the auto financing process for dealers. Our team focuses on creating integrated, secure, and user friendly platforms that enhance dealer operations and customer experiences, ensuring compliance with the latest financial regulations. This pivotal role supports business growth and fosters strong dealer relationships, making every transaction smoother and more efficient. Dealer Tech within Financial Services Technology at Capital One is specifically designed to address the technological needs of auto dealers who partner with Capital One. This division focuses on developing and maintaining systems that facilitate the smooth operation of auto financing, from loan origination to funding. 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: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).