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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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 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.