ML Engineer Jobs at NVIDIA with Visa Sponsorship
ML Engineer jobs at NVIDIA sit at the intersection of GPU architecture, large-scale model training, and production inference systems. The company has a consistent track record of sponsoring work visas for engineers in this function, covering both nonimmigrant and immigrant pathways for qualified candidates.
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Get Access To All JobsINTRODUCTION
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.
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.
See all ML Engineer Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Engineer Jobs at NVIDIA.
Get Access To All JobsTips for Finding ML Engineer Jobs at NVIDIA
Align your portfolio with NVIDIA's research stack
NVIDIA ML Engineer roles typically require hands-on experience with CUDA, TensorRT, or large-scale distributed training. Frame your GitHub projects and publications around GPU-accelerated workloads before applying, so your credentials match the technical bar reviewers expect.
Target teams where E-3 eligibility fits
If you hold Australian citizenship, the E-3 visa pathway lets NVIDIA sponsor you without lottery risk. Identify open ML Engineer requisitions in hardware-adjacent teams like CUDA Libraries or AI Infrastructure, where Australian candidates have historically been placed.
Understand NVIDIA's internal visa timeline
NVIDIA typically initiates H-1B cap filings in March for an October 1 start. If you receive an offer after the lottery, ask your recruiter whether a cap-exempt entity or bridge arrangement is available to cover the gap period before your start date.
Prepare for speciality occupation scrutiny early
USCIS may issue an RFE if your ML Engineer title appears generalist. Before your offer letter is finalized, confirm the job description explicitly requires a degree in computer science, electrical engineering, or a directly related field, not just any technical bachelor's degree.
Use Migrate Mate to filter verified sponsoring ML roles
Browsing open roles by function and sponsorship type saves significant time. Use Migrate Mate to filter ML Engineer positions at companies with confirmed H-1B and E-3 sponsorship histories, so you apply where the pathway already exists.
Plan your Green Card timeline from day one
NVIDIA sponsors EB-2 and EB-3 PERM petitions for ML Engineers, but PERM labor certification typically takes 12 to 18 months before an I-140 is filed. Ask your recruiter when the company typically initiates PERM for your country of birth, since priority date backlogs vary significantly.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for ML Engineers?
Yes, NVIDIA sponsors H-1B visas for ML Engineer roles. The company participates in the annual H-1B cap lottery, with registrations submitted in March for an October 1 start date. If you're already in H-1B status with another employer, NVIDIA can file an H-1B transfer so you can start before October 1 without waiting for the next cap cycle.
How do I apply for ML Engineer jobs at NVIDIA?
Apply directly through NVIDIA's careers portal, filtering by the Machine Learning or AI Engineering job family. Tailor your resume to reflect GPU computing, model optimization, or distributed training experience relevant to the specific team. You can also browse verified ML Engineer openings at NVIDIA with confirmed sponsorship eligibility through Migrate Mate before applying.
Which visa types does NVIDIA sponsor for ML Engineers?
NVIDIA sponsors H-1B visas for ML Engineers under the specialty occupation category. Australian citizens can pursue the E-3 visa, which has no lottery and allows two-year renewable status. For permanent residence, NVIDIA supports EB-2 and EB-3 Green Card pathways through the PERM labor certification process filed with the DOL.
What qualifications does NVIDIA expect for ML Engineer roles?
NVIDIA ML Engineer roles typically require a bachelor's, master's, or PhD in computer science, electrical engineering, or a closely related field, with strong emphasis on GPU programming, deep learning frameworks such as PyTorch or JAX, and production model deployment. Candidates with published research or contributions to open-source ML infrastructure tend to move faster through the technical screen process.
How do I manage my visa status while waiting for an H-1B approval at NVIDIA?
If you're transitioning from OPT or another nonimmigrant status, timing matters. NVIDIA can file your H-1B with premium processing through USCIS, which reduces the adjudication window to 15 business days. If your OPT expires before October 1, ask your immigration contact at NVIDIA whether a cap-gap extension or a bridge to another status is available to maintain continuous work authorization.