ML Software Engineer Visa Sponsorship Jobs in Virginia
Virginia is one of the strongest states for ML software engineer visa sponsorship, anchored by a dense corridor of tech employers in Northern Virginia, including Amazon Web Services, Booz Allen Hamilton, Leidos, and Capital One. The state's proximity to federal agencies and defense contractors creates consistent demand for ML engineers with security-eligible backgrounds.
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Get Access To All JobsINTRODUCTION
We are seeking a Software Engineer II, Machine Learning Systems, to help deploy and operationalize machine learning solutions used in non-destructive testing applications within the nuclear industry. In this role, you will support the development of machine learning workflows by ensuring they can be packaged, deployed, and maintained in reproducible, stable environments for pilot and production use. This is not a research-focused data science role. Instead, the position centers on machine learning system integration, deployment, infrastructure awareness, and engineering execution. You will work closely with data scientists and subject-matter experts to validate solutions, implement deployment best practices, and integrate machine learning capabilities into existing systems. The environment includes on-premises, regulated, and occasionally air-gapped systems where considerations such as networking, hardware constraints, dependency management, and operational reliability are critical. Projects may involve visual inspection data, ultrasonic signals, eddy current inspection data, and other scientific or industrial data formats. Early projects will focus on executing well-defined deployment and integration efforts. As the team grows, you will have opportunities to help shape deployment standards, reproducibility practices, and system integration patterns across multiple machine learning initiatives.
BASIC QUALIFICATIONS
- Bachelor's degree in Computer Science, Physics, Software Engineering, Applied Mathematics, Data Science, or a related technical field. Advanced degrees are welcome but not required.
- 2+ years of professional software engineering experience.
- At least 1 year of hands-on experience supporting machine learning or data-driven applications.
- Experience packaging, deploying, or supporting Python-based applications outside of notebook environments.
- Experience working across Windows and Linux environments.
- Experience working in on-premises, air-gapped, regulated, or otherwise constrained environments.
- Understanding of machine learning fundamentals and the requirements for training, inference, and deployment workflows.
- Ability to collaborate with data scientists, subject-matter experts, and engineering teams to support production-ready ML workflows.
PREFERRED QUALIFICATIONS
- Experience building ETL workflows or data adapters for scientific or industrial datasets.
- Experience deploying ML applications in air-gapped or constrained environments.
- Experience integrating ML workflows with legacy applications.
- Experience implementing logging, observability, error handling, and performance monitoring.
- Experience with object storage, dataset versioning, artifact management, or model synchronization workflows.
- Experience supporting distributed systems or deployments that span multiple machines.
- Experience working with binary data such as images, video, signals, ultrasonic inspection data, or eddy current inspection data.
- Experience with Microsoft Azure.
- Experience with C++ or C#.
- Experience supporting UI-facing applications.
INTRODUCTION
We are seeking a Software Engineer II, Machine Learning Systems, to help deploy and operationalize machine learning solutions used in non-destructive testing applications within the nuclear industry. In this role, you will support the development of machine learning workflows by ensuring they can be packaged, deployed, and maintained in reproducible, stable environments for pilot and production use. This is not a research-focused data science role. Instead, the position centers on machine learning system integration, deployment, infrastructure awareness, and engineering execution. You will work closely with data scientists and subject-matter experts to validate solutions, implement deployment best practices, and integrate machine learning capabilities into existing systems. The environment includes on-premises, regulated, and occasionally air-gapped systems where considerations such as networking, hardware constraints, dependency management, and operational reliability are critical. Projects may involve visual inspection data, ultrasonic signals, eddy current inspection data, and other scientific or industrial data formats. Early projects will focus on executing well-defined deployment and integration efforts. As the team grows, you will have opportunities to help shape deployment standards, reproducibility practices, and system integration patterns across multiple machine learning initiatives.
BASIC QUALIFICATIONS
- Bachelor's degree in Computer Science, Physics, Software Engineering, Applied Mathematics, Data Science, or a related technical field. Advanced degrees are welcome but not required.
- 2+ years of professional software engineering experience.
- At least 1 year of hands-on experience supporting machine learning or data-driven applications.
- Experience packaging, deploying, or supporting Python-based applications outside of notebook environments.
- Experience working across Windows and Linux environments.
- Experience working in on-premises, air-gapped, regulated, or otherwise constrained environments.
- Understanding of machine learning fundamentals and the requirements for training, inference, and deployment workflows.
- Ability to collaborate with data scientists, subject-matter experts, and engineering teams to support production-ready ML workflows.
PREFERRED QUALIFICATIONS
- Experience building ETL workflows or data adapters for scientific or industrial datasets.
- Experience deploying ML applications in air-gapped or constrained environments.
- Experience integrating ML workflows with legacy applications.
- Experience implementing logging, observability, error handling, and performance monitoring.
- Experience with object storage, dataset versioning, artifact management, or model synchronization workflows.
- Experience supporting distributed systems or deployments that span multiple machines.
- Experience working with binary data such as images, video, signals, ultrasonic inspection data, or eddy current inspection data.
- Experience with Microsoft Azure.
- Experience with C++ or C#.
- Experience supporting UI-facing applications.
ML Software Engineer Job Roles in Virginia
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Search ML Software Engineer Jobs in VirginiaML Software Engineer Jobs in Virginia: Frequently Asked Questions
Which companies sponsor visas for ML software engineers in Virginia?
Northern Virginia hosts some of the most active ML engineering sponsors in the country. Amazon Web Services (with its HQ2 presence in Arlington), Capital One, Booz Allen Hamilton, Leidos, and Accenture Federal Services regularly sponsor H-1B visas for ML roles. Smaller AI-focused firms and government contractors in the Reston and Tysons corridor also sponsor consistently, particularly for applied ML and NLP engineering positions.
Which visa types are most common for ML software engineer roles in Virginia?
The H-1B is by far the most common visa category for ML software engineers in Virginia, as ML engineering qualifies as a specialty occupation requiring at least a bachelor's degree in computer science, mathematics, or a related field. Some candidates at large multinational firms pursue L-1B visas for intracompany transfers. Graduates on OPT or STEM OPT frequently work in Virginia ML roles before transitioning to H-1B sponsorship.
How to find ml software engineer visa sponsorship jobs in Virginia?
Migrate Mate is built specifically for international candidates seeking visa sponsorship and lets you filter ML software engineer roles by state, so you can focus exclusively on Virginia employers actively sponsoring. Because many Virginia ML roles are tied to defense or federal contracting, filtering by employer type on Migrate Mate can help you identify which companies are actively filing sponsorship petitions for this role.
Which cities in Virginia have the most ML software engineer sponsorship jobs?
Northern Virginia, particularly Arlington, McLean, Reston, and Tysons, concentrates the vast majority of ML engineering sponsorship activity in the state. This corridor sits adjacent to Washington D.C. and hosts dense clusters of tech firms, federal contractors, and consultancies. Richmond has a growing presence, driven by Capital One's headquarters and a small but expanding fintech and data engineering sector, though it trails Northern Virginia significantly in volume.
Are there any Virginia-specific considerations for ML software engineer visa sponsorship?
A notable share of ML engineering roles in Virginia involve work with federal agencies or defense programs, which can require security clearances. Non-U.S. citizens are generally ineligible for most security clearances, so some positions in this corridor are effectively closed to sponsored candidates regardless of visa status. Focusing your search on commercial tech employers, financial services firms, and non-classified consulting practices improves your realistic pool of sponsoring opportunities.
What is the prevailing wage for sponsored ml software engineer 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.