Machine Learning Engineer Jobs at NVIDIA with Visa Sponsorship
NVIDIA's Machine Learning Engineer roles 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, E-3, and Green Card pathways, making it a realistic target for qualified candidates who need work authorization.
See All Machine Learning Engineer at NVIDIA JobsOverview
Showing 5 of 35+ Machine Learning Engineer Jobs at NVIDIA jobs


Have you applied for this role?


Have you applied for this role?


Have you applied for this role?


Have you applied for this role?


Have you applied for this role?
See all 35+ Machine Learning Engineer Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at NVIDIA.
Get Access To All Jobs
We are seeking a Senior Machine Learning Engineer to join our end‑to‑end autonomous driving team! You will help build, train, and deploy large‑scale E2E driving models that leverage VLM/VLA architectures, and build a data flywheel that continuously improves our systems in the real world! Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
What you’ll be doing:
- Designing, implementing, and training large‑scale end‑to‑end driving models.
- Driving the data flywheel: identifying failure cases, specifying data collection and labeling needs, and iterating models to close real‑world performance gaps.
- Building, curating, and maintaining high‑quality multimodal datasets (e.g., video, sensor, language/action traces) tailored for end‑to‑end autonomous driving.
- Developing and applying data‑centric learning algorithms such as active learning, curriculum learning, automated hard‑example mining, outlier and novelty detection, and semi/self‑supervised methods.
- Exploring and productizing new data sources including simulation, synthetic data, and world‑model‑based generation/augmentation to improve coverage and robustness.
- Designing and implementing agentic data workflows that automate data discovery, labeling, evaluation, and retraining to maximize development velocity.
- Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.
What we need to see:
- PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field
- Strong background in modern deep learning, including transformer‑based architectures, video modeling, and multimodal VLM/VLA or foundation models.
- Hands‑on experience training and deploying deep learning models on real‑world datasets: data preprocessing, distributed training, evaluation, debugging, and iterative improvement.
- Practical experience with at least some data‑centric methods such as active learning, curriculum learning, outlier/novelty detection, or large‑scale sample mining.
- Proficiency in Python and at least one major deep learning framework (PyTorch, TensorFlow, or JAX), plus solid software engineering practices (testing, code review, CI/CD).
- Demonstrated ability to collaborate effectively across teams, drive designs from prototype to production, and communicate clearly with technical and non‑technical partners.
- Track record of leading complex cross‑team projects, setting technical direction, and making critical technical decisions that impact multiple teams or products.
Ways to stand out from the crowd:
- Experience building and operating data flywheels or large‑scale data pipelines for ML, including data quality monitoring and continuous retraining loops.
- Direct experience with end‑to‑end driving models, large‑scale behavior cloning, or reinforcement/imitation learning for driving or robotics.
- Experience leveraging simulation, synthetic data, or world models to generate training and evaluation data for autonomous systems.
- Contributions to sophisticated methods in data‑centric ML, VLM/VLA, or autonomous driving, such as impactful publications, open‑source projects, or widely used internal tools.
- Background with safety, reliability, and validation requirements for autonomous driving or other safety‑critical applications.
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 April 25, 2026. This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse 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.

We are seeking a Senior Machine Learning Engineer to join our end‑to‑end autonomous driving team! You will help build, train, and deploy large‑scale E2E driving models that leverage VLM/VLA architectures, and build a data flywheel that continuously improves our systems in the real world! Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
What you’ll be doing:
- Designing, implementing, and training large‑scale end‑to‑end driving models.
- Driving the data flywheel: identifying failure cases, specifying data collection and labeling needs, and iterating models to close real‑world performance gaps.
- Building, curating, and maintaining high‑quality multimodal datasets (e.g., video, sensor, language/action traces) tailored for end‑to‑end autonomous driving.
- Developing and applying data‑centric learning algorithms such as active learning, curriculum learning, automated hard‑example mining, outlier and novelty detection, and semi/self‑supervised methods.
- Exploring and productizing new data sources including simulation, synthetic data, and world‑model‑based generation/augmentation to improve coverage and robustness.
- Designing and implementing agentic data workflows that automate data discovery, labeling, evaluation, and retraining to maximize development velocity.
- Foster collaborative partnerships with our researchers and engineers, transforming innovative research into robust, industrial-strength machine learning models.
What we need to see:
- PhD with 4+ years, MS with 6+ years, or BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field
- Strong background in modern deep learning, including transformer‑based architectures, video modeling, and multimodal VLM/VLA or foundation models.
- Hands‑on experience training and deploying deep learning models on real‑world datasets: data preprocessing, distributed training, evaluation, debugging, and iterative improvement.
- Practical experience with at least some data‑centric methods such as active learning, curriculum learning, outlier/novelty detection, or large‑scale sample mining.
- Proficiency in Python and at least one major deep learning framework (PyTorch, TensorFlow, or JAX), plus solid software engineering practices (testing, code review, CI/CD).
- Demonstrated ability to collaborate effectively across teams, drive designs from prototype to production, and communicate clearly with technical and non‑technical partners.
- Track record of leading complex cross‑team projects, setting technical direction, and making critical technical decisions that impact multiple teams or products.
Ways to stand out from the crowd:
- Experience building and operating data flywheels or large‑scale data pipelines for ML, including data quality monitoring and continuous retraining loops.
- Direct experience with end‑to‑end driving models, large‑scale behavior cloning, or reinforcement/imitation learning for driving or robotics.
- Experience leveraging simulation, synthetic data, or world models to generate training and evaluation data for autonomous systems.
- Contributions to sophisticated methods in data‑centric ML, VLM/VLA, or autonomous driving, such as impactful publications, open‑source projects, or widely used internal tools.
- Background with safety, reliability, and validation requirements for autonomous driving or other safety‑critical applications.
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 April 25, 2026. This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse 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 35+ Machine Learning Engineer at NVIDIA jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer at NVIDIA roles.
Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at NVIDIA Jobs
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.
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.
Machine Learning Engineer at NVIDIA jobs are hiring across the US. Find yours.
Find Machine Learning Engineer at NVIDIA JobsFrequently 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.
See which Machine Learning Engineer at NVIDIA employers are hiring and sponsoring visas right now.
Search Machine Learning Engineer at NVIDIA Jobs