AI Data Engineer Jobs at NVIDIA with Visa Sponsorship
AI Data Engineer jobs at NVIDIA sit at the intersection of large-scale data infrastructure and applied machine learning, supporting teams building some of the most compute-intensive systems in the industry. NVIDIA has a consistent track record of sponsoring work visas for this function, covering H-1B visa, E-3 visa, and permanent residence pathways.
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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 AI Data Engineer Jobs at NVIDIA
Align your portfolio with NVIDIA's data stack
NVIDIA's AI Data Engineer roles consistently prioritize hands-on experience with GPU-accelerated data pipelines, RAPIDS, and large-scale feature engineering. Build and document projects using these tools before applying so your work speaks directly to their technical bar.
Target teams that file E-3 petitions directly
NVIDIA processes E-3 petitions in-house rather than routing through a staffing firm, which matters for Australians. Your offer letter and Labor Condition Application come from NVIDIA itself, giving you a cleaner record for future renewals and green card sponsorship.
Flag your visa type early in recruiter conversations
NVIDIA recruiters handle high application volumes for AI Data Engineer roles. Disclosing your H-1B or E-3 requirement before the technical screen lets the recruiting coordinator loop in immigration counsel early and avoids delays after an offer is extended.
Understand how NVIDIA structures PERM for this role
EB-2 and EB-3 PERM filings for AI Data Engineers at NVIDIA typically require demonstrating that the role demands a specific technical degree, not just any bachelor's. Review how DOL defines minimum requirements for your job title before your offer negotiation.
Use Migrate Mate to surface open AI Data Engineer roles at NVIDIA
Roles at NVIDIA that carry active visa sponsorship don't always stay open long. Browse AI Data Engineer listings at NVIDIA on Migrate Mate, which filters specifically for sponsored positions so you're not wasting applications on roles that won't support your visa category.
Prepare your credential documentation before the offer stage
NVIDIA's immigration team will need certified transcripts and, for candidates with three-year degrees, a credential evaluation confirming U.S. equivalency. Commission that evaluation through a NACES-member service before you receive an offer so you're not holding up the I-129 filing.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for AI Data Engineers?
Yes, NVIDIA sponsors H-1B visas for AI Data Engineer roles. NVIDIA participates in the annual H-1B cap lottery each spring, and candidates with a qualifying offer can be registered. If you're already in H-1B status with another employer, NVIDIA can also file an H-1B transfer, letting you start without waiting for a new cap cycle.
Which visa types does NVIDIA commonly use for AI Data Engineer roles?
NVIDIA sponsors H-1B and E-3 visas for AI Data Engineers at the nonimmigrant level, with E-3 available exclusively to Australian citizens. For permanent residence, NVIDIA files EB-2 and EB-3 petitions through the PERM labor certification process. The pathway offered generally depends on your nationality, current status, and how long you've been with the company.
What qualifications does NVIDIA expect for AI Data Engineer roles?
NVIDIA's AI Data Engineer roles typically require a bachelor's or master's degree in computer science, electrical engineering, or a closely related technical field. Practical experience with distributed data systems, ML pipelines, and GPU computing frameworks matters as much as credentials. Candidates who can demonstrate production-scale work, rather than academic projects only, tend to advance further in the technical interview process.
How do I apply for AI Data Engineer jobs at NVIDIA?
You can browse open AI Data Engineer positions at NVIDIA through Migrate Mate, which surfaces roles that include visa sponsorship. Once you identify a role, apply directly through NVIDIA's careers portal. Tailor your resume to reflect the specific data infrastructure and ML tooling called out in the job description, and be prepared for multiple technical rounds assessing systems design and applied machine learning.
How do I understand the timeline from offer to work authorization at NVIDIA?
For H-1B transfers or cap-exempt filings, USCIS standard processing runs three to six months, though NVIDIA commonly uses premium processing to compress that to 15 business days. E-3 applicants pursuing consular processing in Australia can often complete the visa interview within a few weeks of the Labor Condition Application being certified by DOL. Starting the immigration process immediately after signing your offer avoids unnecessary gaps in authorization.