ML Software Engineer Jobs in USA with Visa Sponsorship
ML Software Engineer roles are among the most actively sponsored positions in the U.S. tech industry. Employers regularly file H-1B visa and O-1 visa petitions for qualified candidates, and specialty occupation approval rates for machine learning roles are high given the clear degree-to-role alignment. For detailed occupation requirements, see the O*NET profile.
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INTRODUCTION
At NVIDIA, we’re not just building the future, we’re generating it! Our Cosmos team is pushing the boundaries of multimodal AI, simulation, and world models. As we enter the next phase, we are building agentic systems that can reason about, build, evaluate, and improve AI systems themselves. We are building systems where AI doesn’t just run models but helps build them. This role is about creating the meta-layer of modern ML: the agents, tooling, pipelines, and feedback loops that make model development faster, smarter, and increasingly automated. Rather than focusing on inventing individual model architectures, you will build the systems that help models and teams improve continuously. We are looking for exceptional engineers who are passionate about the idea of AI-native software engineering: systems where agents can work with code, data, experiments, and evaluations to accelerate how machine learning gets done.
ROLE AND RESPONSIBILITIES
What You’ll Be Doing
- Design and implement agentic workflows across the ML lifecycle, including data generation and curation, evaluation, debugging, training orchestration, and iteration.
- Build AI-native systems in which models and agents can interact with codebases, tools, experiments, and environments to improve developer and researcher productivity.
- Create self-improving loops where agents help generate data, surface failures, evaluate outputs, and drive better decisions across the system.
- Own and evolve large-scale Python and PyTorch codebases, turning fast-moving ideas into robust, modular, reusable software.
- Design and scale evaluation platforms that combine automated metrics, human feedback, and agent-driven analysis.
- Build and maintain multimodal ML pipelines spanning data processing, experimentation, benchmarking, and deployment.
- Integrate open-source and internal components into unified systems that enable rapid experimentation and reliable iteration.
- Raise the bar on engineering excellence across the team through strong practices in testing, reproducibility, packaging, code health, and maintainability.
BASIC QUALIFICATIONS
What We Need To See
- Significant experience building machine learning systems and software platforms, not only models.
- Expert-level Python skills, with strong judgment around modularity, abstraction boundaries, and long-term code health.
- Deep familiarity with PyTorch, including the ability to debug, adapt, and extend model behavior within larger software systems.
- Experience building pipelines, evaluation systems, developer tooling, or workflow automation for ML at meaningful scale.
- Strong software engineering fundamentals, including system design, testing, packaging, debugging, and collaborative codebase evolution.
- Strong agency in LLM-based systems, such as tool use, planning, multi-step workflows, code agents, or automation over data and experiments.
- Comfort operating in fast-moving environments where ambiguous ideas must be turned into useful systems quickly.
- BS, MS, or equivalent experience in Computer Science, Engineering, or a related field.
- 12+ years of relevant software development experience.
PREFERRED QUALIFICATIONS
Ways To Stand Out From The Crowd
- You have built agent-based systems that do real work: coding, evaluation, data generation, triage, experimentation, or orchestration.
- You have contributed to impactful open-source ML, Python, or developer tooling.
- Background with context compression and agent memory techniques.
- Familiarity with agent safety and agent identity (AuthN, AuthZ, IAM).
- You bring a high bar for software craftsmanship, but know how to apply it in research-adjacent environments without slowing innovation down.
COMPENSATION
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 26, 2026. This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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Get Access To All JobsTips for Finding Visa Sponsorship as a ML Software Engineer
Lead with your tech stack, not your job title
Hiring managers sponsoring ML engineers want to see PyTorch, TensorFlow, or JAX upfront. Listing frameworks before your title signals technical depth immediately and separates you from generalist software engineers competing for the same roles.
Target companies with an active H-1B filing history
Sponsorship willingness varies significantly by employer. Focus on companies that have consistently filed H-1B petitions for ML roles in prior years. This history is public record and tells you more than any recruiter's vague assurance about sponsorship.
Frame your degree as directly tied to the role
USCIS scrutinizes specialty occupation claims. If your degree is in computer science, statistics, or a related quantitative field, make that connection explicit in applications. Ambiguous degree-to-role alignment is the most common reason ML petitions face additional review.
Highlight published research or patents if you have them
Publications and patents strengthen both H-1B specialty occupation arguments and O-1A extraordinary ability petitions. Even a single co-authored paper on a relevant ML topic adds meaningful credibility to a sponsorship package for this role.
Apply to roles that specify a master's or PhD requirement
Postings requiring an advanced degree in machine learning or AI signal stronger sponsorship intent and clearer specialty occupation documentation. These roles also tend to move faster through USCIS review because the degree-to-role connection is harder to dispute.
Prepare for a longer hiring timeline when sponsorship is involved
H-1B cap-subject petitions must align with the April lottery window. If you're interviewing outside that cycle, ask employers about cap-exempt options, O-1 alternatives, or transfer from an existing valid status to avoid gaps in work authorization.
Frequently Asked Questions
Do ML Software Engineer roles qualify as H-1B specialty occupations?
Yes. Machine learning engineering consistently qualifies as a specialty occupation because the role requires at minimum a bachelor's degree in a specific field such as computer science, mathematics, or statistics. USCIS has approved H-1B visa petitions for ML engineers at high rates when the job description clearly requires theoretical knowledge applied to complex model development, not just general software development tasks.
What degree do I need for an employer to sponsor my H-1B as an ML Software Engineer?
A bachelor's degree or higher in computer science, electrical engineering, mathematics, statistics, or a closely related quantitative field is the standard requirement. A general business or unrelated degree makes sponsorship significantly harder because USCIS will question whether the role genuinely requires that specific academic background. Some employers also accept foreign three-year degrees if accompanied by relevant graduate study or substantial work experience.
Are ML Software Engineer roles eligible for the O-1A visa as an alternative to H-1B?
Yes, and it's a strong alternative for candidates with a research background. The O-1A requires demonstrating extraordinary ability through criteria like published papers, conference presentations, high compensation relative to peers, or judging others' work. ML engineers with NeurIPS, ICML, or ICLR publications, or those who have contributed to widely adopted open-source models, often meet multiple criteria without needing the H-1B lottery.
How competitive is H-1B sponsorship for ML roles compared to other software engineering positions?
ML Software Engineer roles attract sponsorship from a wide range of employers, including large tech companies and well-funded AI startups, which increases your options relative to more generalist engineering titles. However, because demand for these roles is high, cap-subject H-1B slots fill quickly. Candidates who browse ML-specific openings on Migrate Mate can filter for employers with confirmed sponsorship history, which meaningfully improves selection odds.
Can I switch employers on an H-1B as an ML Software Engineer?
Yes. H-1B portability allows you to start working for a new employer as soon as the transfer petition is filed, without waiting for approval, provided you've been in valid H-1B status. The new employer must file a fresh I-129 petition with a new Labor Condition Application reflecting the updated role and location. For ML roles, the specialty occupation argument typically transfers cleanly as long as the new position has comparable technical requirements.
What is the prevailing wage requirement for sponsored ML Software Engineer jobs?
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.