Machine Learning Jobs at NVIDIA with Visa Sponsorship
NVIDIA's Machine Learning teams work 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, E-3, and Green Card pathways for this function.
See All Machine Learning at NVIDIA JobsOverview
Showing 5 of 35+ Machine Learning 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 Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning 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 at NVIDIA jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning at NVIDIA roles.
Get Access To All JobsTips for Finding Machine Learning Jobs at NVIDIA Jobs
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.
Machine Learning at NVIDIA jobs are hiring across the US. Find yours.
Find Machine Learning at NVIDIA JobsFrequently 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.
See which Machine Learning at NVIDIA employers are hiring and sponsoring visas right now.
Search Machine Learning at NVIDIA Jobs