Machine Learning Engineer Jobs at NVIDIA with Visa Sponsorship
Machine Learning Engineer jobs at NVIDIA sit at the intersection of GPU architecture, large-scale model training, and applied AI research. The company has a strong track record of sponsoring international engineers across H-1B visa, E-3 visa, and Green Card pathways, making it a realistic target for qualified candidates who need work authorization.
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Get Access To All JobsAt NVIDIA, we’re building GeForce G-Assist — an on-device AI assistant that combines Small Language Models (SLMs), retrieval systems, and hybrid cloud capabilities to deliver responsive, context-aware assistance inside the GeForce ecosystem. We work closely across engineering and product teams to ensure G-Assist performs reliably in real-world scenarios.
What you'll be doing:
- Together, we focus on how models behave in production, not just on benchmarks. Evaluate and improve Small Language Models used in GeForce G-Assist, with an emphasis on accuracy, robustness, and conversational reliability. Identify and mitigate conversation and context contamination, including state drift, prompt leakage, and retrieval cross-talk.
- Work with SLM and VLM architectures to support text and multimodal interactions. Collaborate on hybrid architectures that combine local SLMs with cloud-based models. We value engineers who enjoy thinking across the full system—from model behavior to runtime performance.
- Optimize local inference using llama.cpp, including quantization, memory usage, and performance tuning. Read, write, and optimize C/C++ code in performance-critical paths.
- Design and integrate retrieval-augmented generation (RAG) systems that ground responses in system and user context. Support agentic AI workflows, enabling planning, tool use, and multi-step execution.
What we need to see:
- 5+ years of validated experience in system software or a related field, with an M.S. or higher degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience). We’re looking for teammates who enjoy solving real problems, learning as they go, and collaborating in a tight-knit environment.
- Strong ability to read and write C/C++ code in systems-level or performance-sensitive environments, along with proficiency in Python. Hands-on experience with llama.cpp or similar local inference frameworks.
- Hands-on experience evaluating Small Language Models, including task-based and conversational testing, with an understanding of conversation dynamics, long-context behavior, and contamination challenges.
- Knowledge of SLM and VLM architectures and their trade-offs, experience with retrieval technologies and language-model integration, and familiarity with agentic AI patterns such as tool use and planning.
Ways to stand out from the crowd:
- Experience contributing to language or multimodal models that power user-facing products, features, or workflows.
- A track record of collaborating with product, platform, or systems teams to balance model capability, performance, and user experience.
- Demonstrated ability to translate user needs or feedback into measurable improvements in model behavior or system reliability.
We are widely considered to be one of the technology world's most desirable employers, and as a result, we have some of the most forward-thinking and hardworking people in the world working for us. If you're passionate, creative, and driven, we'd love to have you join the team. With competitive salaries and a generous benefits package, we are considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us, and due to unprecedented growth, our exclusive engineering teams are rapidly growing. We want to hear from you if you're a creative and autonomous engineer with a real passion for technology.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until October 20, 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.
At NVIDIA, we’re building GeForce G-Assist — an on-device AI assistant that combines Small Language Models (SLMs), retrieval systems, and hybrid cloud capabilities to deliver responsive, context-aware assistance inside the GeForce ecosystem. We work closely across engineering and product teams to ensure G-Assist performs reliably in real-world scenarios.
What you'll be doing:
- Together, we focus on how models behave in production, not just on benchmarks. Evaluate and improve Small Language Models used in GeForce G-Assist, with an emphasis on accuracy, robustness, and conversational reliability. Identify and mitigate conversation and context contamination, including state drift, prompt leakage, and retrieval cross-talk.
- Work with SLM and VLM architectures to support text and multimodal interactions. Collaborate on hybrid architectures that combine local SLMs with cloud-based models. We value engineers who enjoy thinking across the full system—from model behavior to runtime performance.
- Optimize local inference using llama.cpp, including quantization, memory usage, and performance tuning. Read, write, and optimize C/C++ code in performance-critical paths.
- Design and integrate retrieval-augmented generation (RAG) systems that ground responses in system and user context. Support agentic AI workflows, enabling planning, tool use, and multi-step execution.
What we need to see:
- 5+ years of validated experience in system software or a related field, with an M.S. or higher degree in Computer Science, Data Science, Engineering, or a related field (or equivalent experience). We’re looking for teammates who enjoy solving real problems, learning as they go, and collaborating in a tight-knit environment.
- Strong ability to read and write C/C++ code in systems-level or performance-sensitive environments, along with proficiency in Python. Hands-on experience with llama.cpp or similar local inference frameworks.
- Hands-on experience evaluating Small Language Models, including task-based and conversational testing, with an understanding of conversation dynamics, long-context behavior, and contamination challenges.
- Knowledge of SLM and VLM architectures and their trade-offs, experience with retrieval technologies and language-model integration, and familiarity with agentic AI patterns such as tool use and planning.
Ways to stand out from the crowd:
- Experience contributing to language or multimodal models that power user-facing products, features, or workflows.
- A track record of collaborating with product, platform, or systems teams to balance model capability, performance, and user experience.
- Demonstrated ability to translate user needs or feedback into measurable improvements in model behavior or system reliability.
We are widely considered to be one of the technology world's most desirable employers, and as a result, we have some of the most forward-thinking and hardworking people in the world working for us. If you're passionate, creative, and driven, we'd love to have you join the team. With competitive salaries and a generous benefits package, we are considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us, and due to unprecedented growth, our exclusive engineering teams are rapidly growing. We want to hear from you if you're a creative and autonomous engineer with a real passion for technology.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until October 20, 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.
See all Machine Learning Engineer Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at NVIDIA.
Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at NVIDIA
Align your portfolio to NVIDIA's research priorities
NVIDIA hires Machine Learning Engineers for specific workstreams: CUDA optimization, transformer model training, and inference at scale. Structure your GitHub portfolio and resume around these areas before applying, not after you get a recruiter call.
Target roles that map to your visa category
NVIDIA sponsors H-1B, E-3, and EB-2 or EB-3 Green Card pathways, but not every open role is positioned for all three. If you hold an Australian passport, E-3 positions move faster through the system and bypass the H-1B lottery entirely.
Prepare for a multi-round technical screen early
NVIDIA's Machine Learning Engineer interviews typically include systems design, ML theory, and hands-on coding. Have documented project work ready that demonstrates distributed training or low-level GPU programming before your first screen.
Clarify sponsorship timing during the offer stage
Ask your recruiter whether the role is approved for cap-subject H-1B filings or cap-exempt. NVIDIA occasionally hires through affiliated research entities, which changes whether USCIS's October 1 start date applies to your situation.
Use Migrate Mate to filter verified NVIDIA openings
Search Migrate Mate to find Machine Learning Engineer postings at NVIDIA filtered by visa type. This lets you confirm which roles are actively seeking sponsored candidates before investing time in a full application.
Request your LCA wage tier before negotiating salary
NVIDIA files Labor Condition Applications with the DOL that certify a prevailing wage level for each role. Knowing which wage level your offer is benchmarked against helps you negotiate within a defensible range before your employer submits the LCA.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for Machine Learning Engineers?
Yes, NVIDIA sponsors H-1B visas for Machine Learning Engineers. The company files petitions through the standard USCIS cap process each April, with an October 1 employment start date for selected candidates. If you already hold H-1B status with another employer, NVIDIA can file a transfer petition outside the annual lottery window, which is worth raising with your recruiter early.
Which visa types does NVIDIA commonly sponsor for Machine Learning Engineer roles?
NVIDIA sponsors H-1B and E-3 visas for Machine Learning Engineers, along with EB-2 and EB-3 Green Card pathways for longer-term sponsorship. Australian citizens can pursue the E-3, which has no lottery and allows two-year renewable status. H-1B remains the primary pathway for most other nationalities. Green Card sponsorship through PERM typically begins after you've established yourself in the role.
What qualifications does NVIDIA expect for a Machine Learning Engineer position?
Most Machine Learning Engineer roles at NVIDIA require a graduate degree in computer science, electrical engineering, or a related field, along with hands-on experience in deep learning frameworks like PyTorch or JAX. Roles focused on infrastructure or inference optimization often require familiarity with CUDA or Triton. Research-adjacent positions may expect published work or contributions to open-source ML projects.
How do I apply for Machine Learning Engineer jobs at NVIDIA?
Apply directly through NVIDIA's careers portal after identifying roles that match your background and visa eligibility. You can find Machine Learning Engineer openings at NVIDIA that are open to sponsored candidates on Migrate Mate, which filters listings by visa type so you're not applying blind. Tailor your resume to the specific workstream, whether that's model training, inference, or CUDA development, before submitting.
How do I plan my timeline if I need H-1B sponsorship at NVIDIA?
The H-1B cap opens for registration each March, with selected candidates eligible to start October 1. If you're targeting NVIDIA, aim to have an offer finalized before late February so your employer can register you in time. If you're on F-1 OPT, confirm your OPT expiration date and whether you qualify for the 24-month STEM extension, which gives you more runway if you're not selected in the first lottery.