Machine Learning Engineer Jobs at NVIDIA with Visa Sponsorship
Machine Learning Engineer jobs at NVIDIA 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 visa, E-3 visa, and Green Card pathways, making it a realistic target for qualified candidates who need work authorization.
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Get Access To All JobsINTRODUCTION
Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. Now, NVIDIA’s GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world.
We are now looking for an extraordinary Senior Perception Engineer to develop and productize NVIDIA’s autonomous driving solutions. As a member of our perception team, you will be driving E2E solutions for perception modules that are responsible for online mapping — including road layouts, lane structures, boundaries, crosswalks, and other traffic components critical for driving without reliance on HD maps. You will be challenged to improve robustness and accuracy as well as efficiency of the solutions to fully enable autonomous driving anywhere and anytime.
ROLE AND RESPONSIBILITIES
What You’ll Be Doing:
- Designing end2end solutions for Perception and AV stack to enable road network detections across various driving environments from complex intersections to rural curvy roads to multi-level highways.
- Applied research and development of innovative deep learning models for lane graph construction, road boundary detection, traffic element recognition, and other static-world tasks.
- Develop generalizable approaches to support diverse ODDs and Country/region expansion.
- Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, labeling efficiency optimization, so that value of data is maximized.
- Leverage data simulation and augmentation for solving extreme scenarios.
- Productize the developed perception solutions by meeting product requirements for safety, latency, and SW robustness.
BASIC QUALIFICATIONS
What We Need to See:
- Minimum Requirement: 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.
- 2+ years of technical leadership demonstrating high technical and organizational complexity is a big plus.
- Hands-on work experience in developing deep learning and algorithms to solve sophisticated real world problems, and proficiency in using deep learning frameworks (e.g., PyTorch).
- Experience in data-driven development and collaboration with data and ground truth teams.
- Strong programming skills in python and/or C++.
- Outstanding communication and teamwork skills as we work as a tightly-knit team, always discussing and learning from each other.
PREFERRED QUALIFICATIONS
Ways to Stand Out from the Crowd:
- Proven expertise in developing generalizable perception solutions for autonomous driving or robotics using deep learning with cameras.
- Hands-on experience in developing and deploying DNN-based solutions to embedded platforms for real time applications.
- Proven expertise in deep learning backed up by technical publications in leading conferences/journals.
- Expertise with Transformers, BEV architectures, and modern static-world perception techniques. Experience in working on similar online mapping and complex road detection problems is a big plus.
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 June 8, 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
Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. Now, NVIDIA’s GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world.
We are now looking for an extraordinary Senior Perception Engineer to develop and productize NVIDIA’s autonomous driving solutions. As a member of our perception team, you will be driving E2E solutions for perception modules that are responsible for online mapping — including road layouts, lane structures, boundaries, crosswalks, and other traffic components critical for driving without reliance on HD maps. You will be challenged to improve robustness and accuracy as well as efficiency of the solutions to fully enable autonomous driving anywhere and anytime.
ROLE AND RESPONSIBILITIES
What You’ll Be Doing:
- Designing end2end solutions for Perception and AV stack to enable road network detections across various driving environments from complex intersections to rural curvy roads to multi-level highways.
- Applied research and development of innovative deep learning models for lane graph construction, road boundary detection, traffic element recognition, and other static-world tasks.
- Develop generalizable approaches to support diverse ODDs and Country/region expansion.
- Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, labeling efficiency optimization, so that value of data is maximized.
- Leverage data simulation and augmentation for solving extreme scenarios.
- Productize the developed perception solutions by meeting product requirements for safety, latency, and SW robustness.
BASIC QUALIFICATIONS
What We Need to See:
- Minimum Requirement: 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.
- 2+ years of technical leadership demonstrating high technical and organizational complexity is a big plus.
- Hands-on work experience in developing deep learning and algorithms to solve sophisticated real world problems, and proficiency in using deep learning frameworks (e.g., PyTorch).
- Experience in data-driven development and collaboration with data and ground truth teams.
- Strong programming skills in python and/or C++.
- Outstanding communication and teamwork skills as we work as a tightly-knit team, always discussing and learning from each other.
PREFERRED QUALIFICATIONS
Ways to Stand Out from the Crowd:
- Proven expertise in developing generalizable perception solutions for autonomous driving or robotics using deep learning with cameras.
- Hands-on experience in developing and deploying DNN-based solutions to embedded platforms for real time applications.
- Proven expertise in deep learning backed up by technical publications in leading conferences/journals.
- Expertise with Transformers, BEV architectures, and modern static-world perception techniques. Experience in working on similar online mapping and complex road detection problems is a big plus.
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 June 8, 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 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 JobsTips for Finding Machine Learning Engineer Jobs at NVIDIA
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.
Target roles that map to your visa category
NVIDIA sponsors H-1B, E-3, and EB-2 or EB-3 Green Card pathways, but not every open role is positioned for all three. If you hold an Australian passport, E-3 positions move faster through the system and bypass the H-1B lottery entirely.
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.
Frequently 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.