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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INTRODUCTION
NVIDIA is seeking talented Deep Learning Scientists / AI Researchers / Machine Learning Engineers to join our rapidly growing AI Safety and Responsibility efforts for Enterprise Risk Management. In this role, you will take on innovative problems in machine learning, focusing specifically on scaling safety for multi-modal Large Language Models (LLMs) including advanced agentic safety.
NVIDIA is in a unique position: we develop AI-based products across multiple domains and collaborate with the world’s leading AI companies as partners and customers. This role is directed at measuring improving the security, content safety, and inclusivity of our frontier models. Because we are expanding across multiple pillars of safety, we are looking for specialists with deep expertise in one or more of the following core focus areas:
- LLM Security: Focus on backdoors, data poisoning, latent malicious behavior, and structural model vulnerabilities.
- Frontier Risks: Focus on advanced alignment challenges, including model deception, manipulation, and loss-of-control scenarios.
- Agentic Safety: Focus on LLM-level safety for autonomous systems, including multi-turn tool-calling, orchestration, and execution risks.
- Multi-turn Safety Evaluation: Focus on robust, scalable automated evaluation methodologies for conversational and iterative multi-turn use cases.
ROLE AND RESPONSIBILITIES
What you'll be doing:
- Evaluation: Develop datasets and specialized models & algorithms to evaluate/benchmark models & end-to-end systems in our core safety tracks (LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness).
- Model Pre-Training, Mid-Training, Post-Training: Develop datasets and recipes for filtering training data, developing training datasets & recipes, including components like RL environments and teacher models, across our core safety tracks (LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness).
- Model & system level techniques beyond post-training: Research & deploy new approaches, like Instruction Hierarchy or Risk Detection.
- Cross-Functional Collaboration: Partner with engineers, data scientists, and research teams across NVIDIA to scale solutions for LLM Security, Agentic Safety, Content Safety, Hallucinations, and ML Fairness.
BASIC QUALIFICATIONS
What we need to see:
- Master’s or PhD in Computer Science, Electrical Engineering, or a related quantitative field (or equivalent experience).
- 8+ years of proven experience in systems software engineering or machine learning engineering.
- Post-Training Experience: 4+ years of hands-on work experience in post-training of LLMs, including Supervised Fine-Tuning (SFT), Reinforcement Learning (RLHF/RLAIF), safety data generation techniques, ablation studies, and deploying models to production.
- Core Safety Expertise: 1+ years of dedicated experience or research in at least one of the following areas:
- LLM Security (backdoors, poisoning, latent behaviors).
- Frontier Risks (deception, manipulation, loss-of-control).
- Agentic Safety (LLM-level risks for multi-turn tool-calling/agents).
- Multi-turn Safety Evaluation (dynamic and multi-turn alignment benchmarks).
- Technical Mastery: In-depth knowledge of machine learning principles and frameworks (PyTorch preferred) with strong Python programming skills.
- Multimodal Systems: Experience working with large multimodal datasets and multi-modal foundational models.
- Soft Skills: Outstanding analytical problem-solving abilities paired with excellent collaboration and communication skills.
- Cultural Alignment: Demonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty.
PREFERRED QUALIFICATIONS
Ways to stand out from the crowd:
- Academic Track Record: Published papers on AI Safety, alignment, or machine learning security as a primary author at top-tier conferences (NeurIPS, ICML, ICLR, ACL, etc.).
- Community Contributions: Active contributions to open-source AI Safety tools, benchmarks, datasets, and/or models.
- Advanced Alignment: Proven experience with alignment/fine-tuning of Vision-Language Models (VLMs) or any-to-text foundational models.
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 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until September 4, 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 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.