ML Engineer Jobs at NVIDIA with Visa Sponsorship
NVIDIA's ML Engineer roles 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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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.
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Get Access To All JobsTips for Finding ML Engineer Jobs at NVIDIA Jobs
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 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.
ML Engineer at NVIDIA jobs are hiring across the US. Find yours.
Find ML Engineer at NVIDIA JobsFrequently 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.
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