Machine Learning Visa Sponsorship Jobs in Virginia
Virginia is one of the strongest states for machine learning visa sponsorship, anchored by the federal technology corridor in Northern Virginia and the AWS, Booz Allen Hamilton, and Leidos presence around Arlington and Tysons. Defense contractors, cloud infrastructure firms, and government IT consultancies drive consistent ML hiring across the state.
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Get Access To All JobsINTRODUCTION
Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences)
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
ROLE AND RESPONSIBILITIES
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:
- Lead dedicated pods of software, data and machine learning engineers in building AI/ML capabilities for Credit and Financial Risk Management products, serving as a technical mentor to the team on these core technologies
- Design, build, and deliver AI-powered products and components that solve real-world business problems, leveraging expertise in model experimentation, LLM inference, similarity search, and agentic AI within a collaborative Product and Data Science environment
- Collaborate with a cross-functional team of engineers, data scientists, and designers to develop and scale AI-powered products that enable optimized associate performance and deliver world-class customer value
- 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
- 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
- Leverage a broad stack of Open Source and SaaS AI technologies and use programming languages like Python, Scala, or Java
BASIC QUALIFICATIONS
- Bachelor’s Degree
- At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- At least 4 years of experience programming with Python, Scala, or Java
- At least 3 years of experience building, scaling, and optimizing ML systems
- At least 2 years of experience leading teams developing ML solutions
PREFERRED QUALIFICATIONS
- Master's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a similar field
- 6+ years of experience designing, developing, delivering, and supporting AI services at scale
- 3+ years of experience developing AI and ML algorithms or technologies using Python
- 2+ years of experience with Retrieval Augmented Generation (RAG)
- Experience staying abreast of latest ML research with an intuitive ability to understand scientific publications and judiciously apply novel techniques in production
- Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure
- Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
COMPENSATION
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
- Cambridge, MA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer
- McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer
- New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer
- Richmond, VA: $209,000 - $238,500 for Sr. Lead 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 4901-4920; 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 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. 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 Careers@capitalone.com.
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).
INTRODUCTION
Senior Lead Machine Learning Engineer (Intelligent Foundations and Experiences)
As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.
ROLE AND RESPONSIBILITIES
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:
- Lead dedicated pods of software, data and machine learning engineers in building AI/ML capabilities for Credit and Financial Risk Management products, serving as a technical mentor to the team on these core technologies
- Design, build, and deliver AI-powered products and components that solve real-world business problems, leveraging expertise in model experimentation, LLM inference, similarity search, and agentic AI within a collaborative Product and Data Science environment
- Collaborate with a cross-functional team of engineers, data scientists, and designers to develop and scale AI-powered products that enable optimized associate performance and deliver world-class customer value
- 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
- 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
- Leverage a broad stack of Open Source and SaaS AI technologies and use programming languages like Python, Scala, or Java
BASIC QUALIFICATIONS
- Bachelor’s Degree
- At least 8 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
- At least 4 years of experience programming with Python, Scala, or Java
- At least 3 years of experience building, scaling, and optimizing ML systems
- At least 2 years of experience leading teams developing ML solutions
PREFERRED QUALIFICATIONS
- Master's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or a similar field
- 6+ years of experience designing, developing, delivering, and supporting AI services at scale
- 3+ years of experience developing AI and ML algorithms or technologies using Python
- 2+ years of experience with Retrieval Augmented Generation (RAG)
- Experience staying abreast of latest ML research with an intuitive ability to understand scientific publications and judiciously apply novel techniques in production
- Experience deploying scalable AI/ML solutions in a public cloud such as AWS Bedrock, Google Cloud, Azure
- Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
COMPENSATION
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
- Cambridge, MA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer
- McLean, VA: $229,900 - $262,400 for Sr. Lead Machine Learning Engineer
- New York, NY: $250,800 - $286,200 for Sr. Lead Machine Learning Engineer
- Richmond, VA: $209,000 - $238,500 for Sr. Lead 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 4901-4920; 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 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. 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 Careers@capitalone.com.
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 Job Roles in Virginia
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Search Machine Learning Jobs in VirginiaMachine Learning Jobs in Virginia: Frequently Asked Questions
Which companies sponsor visas for machine learning roles in Virginia?
Major sponsors include Amazon Web Services (headquartered in Arlington), Booz Allen Hamilton, Leidos, Capital One, and General Dynamics IT. Microsoft and Northrop Grumman also have significant Virginia footprints. Defense and intelligence contractors dominate the state's ML hiring, though financial services firms like Capital One have grown their ML engineering teams substantially in the McLean area.
Which visa types are most common for machine learning roles in Virginia?
The H-1B visa is the most common visa for machine learning professionals in Virginia, given that ML engineer and data scientist roles consistently qualify as specialty occupations requiring at least a bachelor's degree in computer science, statistics, or a related field. Some employers also sponsor O-1A visas for candidates with published research or notable technical achievements. OPT and STEM OPT are common entry points before full H-1B sponsorship.
Which cities in Virginia have the most machine learning sponsorship jobs?
Northern Virginia, particularly Arlington, McLean, Reston, and Tysons, accounts for the majority of ML sponsorship activity in the state, driven by the federal technology corridor and AWS's headquarters. Richmond has a growing ML presence, largely in financial services and healthcare IT. Charlottesville sees some activity tied to the University of Virginia's research ecosystem, though at much smaller volume than Northern Virginia.
How to find machine learning visa sponsorship jobs in Virginia?
Migrate Mate is built specifically for international candidates seeking visa sponsorship, so you can filter directly for machine learning roles in Virginia without sorting through listings from employers who won't sponsor. Virginia's ML market skews heavily toward defense contractors and cloud infrastructure companies, so filtering by industry on Migrate Mate helps identify which employers are actively sponsoring rather than those with occasional openings.
Are there state-specific factors that affect machine learning visa sponsorship in Virginia?
Virginia's concentration of defense and intelligence contractors creates a notable consideration: many ML roles require U.S. security clearances, which visa holders are generally ineligible to obtain. This limits the share of Virginia ML jobs that are realistically open to sponsored candidates compared to states with broader commercial tech sectors. Focusing on commercial employers like AWS, Capital One, and mid-sized software firms in Northern Virginia significantly improves practical access to sponsorship-eligible roles.
What is the prevailing wage for sponsored machine learning jobs in Virginia?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.