AI ML Engineer Jobs at NVIDIA with Visa Sponsorship
AI ML Engineer jobs at NVIDIA sit at the intersection of GPU architecture, large-scale model training, and applied research. The company has a consistent track record of sponsoring work visas for this function, making it a realistic target for international engineers with strong ML credentials.
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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 AI ML Engineer Jobs at NVIDIA
Align your portfolio with NVIDIA's research priorities
NVIDIA's ML engineering roles heavily favor experience with CUDA, transformer architectures, and distributed training at scale. Frame your project portfolio around these specifically before applying, not general ML work.
Target roles within NVIDIA's applied AI teams
NVIDIA hires AI ML Engineers across both research and product-facing teams. Applied teams building inference infrastructure or model optimization tooling tend to move faster through offer and filing stages than pure research tracks.
Prepare your credentials for a specialty occupation determination
USCIS evaluates whether your role qualifies as a specialty occupation under H-1B. For AI ML Engineer positions, document how your degree field, whether computer science, electrical engineering, or applied mathematics, directly maps to the job duties listed in your offer.
Understand NVIDIA's E-3 pathway if you hold Australian citizenship
NVIDIA sponsors E-3 visas, which have no lottery and a faster timeline than H-1B. If you're an Australian citizen, flag this during your interview process so the recruiting team routes you through the correct filing pathway from day one.
Confirm the filing timeline against your current status expiry
If you're on OPT or a 60-day grace period, map your status end date against USCIS H-1B premium processing timelines, currently 15 business days after receipt. NVIDIA's legal team will need lead time, so raise your deadline at the offer stage, not after signing.
Use Migrate Mate to identify open AI ML Engineer roles at NVIDIA
Searching broadly across job boards misses roles that are actively open to visa sponsorship. Use Migrate Mate to filter specifically for AI ML Engineer positions at NVIDIA where H-1B or E-3 sponsorship is confirmed.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for AI ML Engineers?
Yes, NVIDIA sponsors H-1B visas for AI ML Engineers. The company has a well-established immigration process and works with experienced legal counsel to handle H-1B filings. If you receive an offer, NVIDIA's recruiting team will walk you through the sponsorship timeline. Premium processing is available through USCIS if your status deadline is tight.
Which visa types does NVIDIA commonly sponsor for AI ML Engineer roles?
NVIDIA sponsors H-1B visas for most international AI ML Engineer hires. Australian citizens are eligible for the E-3 visa, which skips the lottery and typically processes faster. For engineers on longer-term pathways, NVIDIA also sponsors EB-2 and EB-3 Green Card petitions, including PERM labor certification filings through the DOL.
What qualifications does NVIDIA expect for AI ML Engineer roles?
Most AI ML Engineer openings at NVIDIA require a bachelor's degree at minimum, with many senior roles expecting a master's or PhD in computer science, electrical engineering, or a closely related field. Hands-on experience with GPU computing, large-scale model training, and frameworks like PyTorch or JAX is expected. Practical experience with inference optimization or CUDA programming strengthens applications significantly.
How do I apply for AI ML Engineer jobs at NVIDIA?
You can find and apply for AI ML Engineer roles at NVIDIA directly through NVIDIA's careers portal, or browse verified open roles filtered for visa sponsorship through Migrate Mate. When applying, tailor your resume to reflect experience with distributed training, GPU optimization, or production ML systems. NVIDIA's recruiting process typically includes technical screens followed by multi-round system design and coding interviews.
How do I plan my H-1B filing timeline when targeting NVIDIA?
H-1B cap-subject filings open in March each year, with an October 1 start date. If you're on OPT, confirm your STEM OPT extension is in place to bridge the gap if needed. NVIDIA initiates the employer-side LCA filing with DOL before submitting the H-1B petition to USCIS, so communicate your status expiry to the recruiter as early as the offer stage.