Machine Learning Jobs at NVIDIA with Visa Sponsorship
Machine Learning jobs at NVIDIA involve working on some of the most demanding AI infrastructure and research problems in the industry, from GPU-accelerated model training to production inference systems. NVIDIA has a strong track record of sponsoring international talent across H-1B visa, E-3 visa, and Green Card pathways for this function.
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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 Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Jobs at NVIDIA.
Get Access To All JobsTips for Finding Machine Learning Jobs at NVIDIA
Align your portfolio to NVIDIA's ML stack
NVIDIA recruits for ML roles tied to CUDA, TensorRT, and GPU-accelerated training pipelines. Before applying, make sure your portfolio or GitHub demonstrates hands-on experience with these tools, not just general PyTorch or TensorFlow projects.
Target teams publishing active research
NVIDIA's Applied Deep Learning Research and Autonomous Vehicles groups consistently hire ML engineers and sponsor visas for those roles. Cross-referencing job postings with published papers from those teams helps you identify where active headcount actually exists.
Distinguish your E-3 eligibility early in conversations
If you're an Australian citizen, flag your E-3 eligibility during the recruiter screen. NVIDIA sponsors E-3 visas for ML roles, and because E-3 applications are processed at a consulate without a lottery, the timeline to your start date is significantly shorter than H-1B.
Prepare specialty occupation documentation before your offer
For H-1B sponsorship, USCIS evaluates whether the ML role requires a specific bachelor's degree or higher. Gather transcripts, degree equivalency evaluations, and a clear job description linking your specialization to the role before your offer letter arrives.
Use Migrate Mate to surface NVIDIA ML openings that sponsor
Search Migrate Mate to filter Machine Learning roles at NVIDIA by visa sponsorship type. You can identify which roles are actively sponsored and apply directly, rather than filtering through listings that don't confirm sponsorship upfront.
Understand the PERM timeline if you're targeting a Green Card
NVIDIA sponsors EB-2 and EB-3 Green Cards for ML staff, but the PERM labor certification process through DOL typically takes 12 to 18 months before an I-140 is filed. Factor that into how you think about long-term status planning when evaluating an offer.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for Machine Learnings?
Yes, NVIDIA sponsors H-1B visas for Machine Learning roles. Because H-1B cap-subject petitions are subject to an annual lottery, USCIS registration typically opens in March for an October 1 start date. NVIDIA participates in this process and has a consistent history of sponsoring ML engineers and researchers through both standard and premium processing.
Which visa types does NVIDIA sponsor for Machine Learning roles?
NVIDIA sponsors H-1B visas for most international ML hires and E-3 visas for Australian citizens, which can be processed at a U.S. consulate without a lottery. For longer-term permanent residence, NVIDIA also supports EB-2 and EB-3 Green Card pathways, including PERM labor certification filed through the Department of Labor.
What qualifications does NVIDIA expect for Machine Learning positions?
NVIDIA's ML roles typically require a bachelor's, master's, or PhD in computer science, electrical engineering, or a related field, with hands-on experience in GPU computing, deep learning frameworks, and model optimization. Research-oriented roles often expect published work or demonstrated contributions to open-source ML projects. Industry experience with production-scale inference systems is valued for applied engineering positions.
How do I apply for Machine Learning jobs at NVIDIA?
You can browse and apply for sponsored Machine Learning roles at NVIDIA through Migrate Mate, which filters listings by visa sponsorship type so you can confirm eligibility before applying. From there, applications route through NVIDIA's standard hiring process, which typically includes a recruiter screen, technical assessments, and a system design or research-focused interview loop depending on the role level.
How do I time my application around the H-1B cap and NVIDIA's hiring cycle?
USCIS opens H-1B registration each March, and NVIDIA typically plans offers for international candidates to align with the October 1 cap-subject start date. If you're on OPT, confirm how much runway you have before your authorization expires. Applying in the preceding fall or winter gives NVIDIA time to move through hiring before the registration window opens.