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 in a unique position: we are developing AI-based products across multiple domains, and we collaborate with many interesting AI companies as partners and customers. Ensuring the highest Content Safety possible reduces exposure to inappropriate material. Preventing Bias and Discrimination is essential to both protect individual rights and achieve the best quality of results, including accuracy and completeness of information. By prioritizing safety and fairness, we can ensure that LLMs benefit everyone and contribute to a better future for all.
Our team also works in the area of safety for generative models for language, robustness, and explainability. Our LLMs are a growing area of AI products, including models and services, and we are committed to ensuring that they are used safely and responsibly. We are looking for a talented Machine Learning Engineer to work on Product Security, Content Safety, ML Fairness and Robustness efforts for LLMs across all of our research and production engineering teams. In this role, you’ll have the opportunity to take on innovative problems in machine learning, particularly focused on safety for multi-modal LLMs. This role is directed at assessing, quantifying, and improving the safety and inclusivity of our LLM models in a scalable fashion.
ROLE AND RESPONSIBILITIES
What you'll be doing:
- Develop the datasets and models for training and evaluating models and end-to-end systems for Content Safety, ProdSec, Robustness and ML Fairness.
- Research and implement cutting-edge techniques for bias detection and mitigation in LLMs and systems using LLMs like RAGs.
- Define and track key metrics for responsible LLM behavior and usage.
- Follow the best MLOps practices of automation, monitoring, scale and safety.
- Contribute to the MLOps platform and develop safety tools to help ML teams be more effective.
- Collaborate with other engineers, data scientists, and researchers to develop and implement solutions to content safety and ML fairness challenges.
BASIC QUALIFICATIONS
What we need to see:
- Master’s or PhD in Computer Science, Electrical Engineering or related field - or equivalent experience.
- Minimum of 2+ years of work experience in developing and deploying machine learning models in production.
- Strong understanding of machine learning principles and algorithms.
- Hands-on programming experience in python and in-depth knowledge of machine learning frameworks, like Keras or PyTorch.
- Background in one or more of the following broader areas for 1+ years: Content Safety, ML Fairness, Robustness, AI Model Security, or related areas.
- Experience working in a range of the following areas within Content Safety: Hate/Harassment, Sexualized, Harmful/Violent, or other specific areas from your application.
- Practice working with large multi-modal datasets and multi-modal models.
- Good at problem-solving and analytical ability.
- Excellent collaboration and communication skills.
- Demonstrates behaviors that build trust: humility, transparency, respect, and intellectual honesty.
PREFERRED QUALIFICATIONS
Ways to stand out from the crowd:
- Skilled with alignment/fine-tuning of LLMs - including regular LLMs as well as VLMs (vision-language models) or any-to-text.
- Proven experience with multimodal and/or multilingual content safety, legal, and regulatory compliance.
- Knowledge of robustness, including hallucinations, digressions, and generative misinformation.
- Experience with GenAI security, including prompt stability, model extraction, confidentiality/data extraction, integrity, availability, and adversarial robustness.
- Passion for AI and a demonstrated commitment to advancing the field through innovative research, prior scientific research, and publication experience.
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 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until July 28, 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.