AI ML Engineering Jobs at NVIDIA with Visa Sponsorship
AI ML Engineering jobs at NVIDIA sit at the intersection of GPU architecture, large-scale model training, and production inference systems. NVIDIA has a strong track record of sponsoring international engineers across H-1B visa, E-3 visa, and employment-based Green Card pathways, making it a viable target for visa-dependent candidates with deep ML expertise.
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Get Access To All JobsWe are seeking a Principal AI and ML Infra Software Engineer, GPU Clusters at NVIDIA to join our Hardware Infrastructure team. As an Engineer, you will have a pivotal role in enhancing efficiency for our researchers by implementing progressions throughout the entire stack. Your main task will revolve around collaborating closely with customers to pinpoint and address infrastructure deficiencies, facilitating groundbreaking AI and ML research on GPU Clusters. Together, we can craft potent, effective, and scalable solutions as we mold the future of AI/ML technology!
What you will be doing:
- Engage closely with our AI and ML research teams to discern their infrastructure requirements and barriers, converting those insights into actionable improvements.
- Proactively identify researcher efficiency bottlenecks and lead initiatives to systematically improve it. Drive the direction and long-term roadmaps for such initiatives.
- Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization.
- Help define and improve important measures of AI researcher efficiency, ensuring that our actions are in line with measurable results.
- Work closely with a variety of teams, such as researchers, data engineers, and DevOps professionals, to develop a cohesive AI/ML infrastructure ecosystem.
- Keep up to date with the most recent developments in AI/ML technologies, frameworks, and successful strategies, and advocate for their integration within the organization.
What we need to see:
- BS or similar background in Computer Science or related area (or equivalent experience).
- 15+ years of demonstrated expertise in AI/ML and HPC tasks and systems.
- Hands-on experience in using or operating High Performance Computing (HPC) grade infrastructure as well as in-depth knowledge of accelerated computing (e.g., GPU, custom silicon), storage (e.g., Lustre, GPFS, BeeGFS), scheduling & orchestration (e.g., Slurm, Kubernetes, LSF), high-speed networking (e.g., Infiniband, RoCE, Amazon EFA), and containers technologies (Docker, Enroot).
- Capability in supervising and improving substantial distributed training operations using PyTorch (DDP, FSDP), NeMo, or JAX. Moreover, an in-depth understanding of AI/ML workflows, involving data processing, model training, and inference pipelines.
- Proficiency in programming & scripting languages such as Python, Go, Bash, as well as familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) in addition to experience with parallel computing frameworks and paradigms.
- Dedication to ongoing learning and staying updated on new technologies and innovative methods in the AI/ML infrastructure sector.
- Excellent communication and collaboration skills, with the ability to work effectively with teams and individuals of different backgrounds.
NVIDIA offers competitive salaries and a comprehensive benefits package. Our engineering teams are growing rapidly due to outstanding expansion. If you're a passionate and independent engineer with a love for technology, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until May 1, 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 Principal AI and ML Infra Software Engineer, GPU Clusters at NVIDIA to join our Hardware Infrastructure team. As an Engineer, you will have a pivotal role in enhancing efficiency for our researchers by implementing progressions throughout the entire stack. Your main task will revolve around collaborating closely with customers to pinpoint and address infrastructure deficiencies, facilitating groundbreaking AI and ML research on GPU Clusters. Together, we can craft potent, effective, and scalable solutions as we mold the future of AI/ML technology!
What you will be doing:
- Engage closely with our AI and ML research teams to discern their infrastructure requirements and barriers, converting those insights into actionable improvements.
- Proactively identify researcher efficiency bottlenecks and lead initiatives to systematically improve it. Drive the direction and long-term roadmaps for such initiatives.
- Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization.
- Help define and improve important measures of AI researcher efficiency, ensuring that our actions are in line with measurable results.
- Work closely with a variety of teams, such as researchers, data engineers, and DevOps professionals, to develop a cohesive AI/ML infrastructure ecosystem.
- Keep up to date with the most recent developments in AI/ML technologies, frameworks, and successful strategies, and advocate for their integration within the organization.
What we need to see:
- BS or similar background in Computer Science or related area (or equivalent experience).
- 15+ years of demonstrated expertise in AI/ML and HPC tasks and systems.
- Hands-on experience in using or operating High Performance Computing (HPC) grade infrastructure as well as in-depth knowledge of accelerated computing (e.g., GPU, custom silicon), storage (e.g., Lustre, GPFS, BeeGFS), scheduling & orchestration (e.g., Slurm, Kubernetes, LSF), high-speed networking (e.g., Infiniband, RoCE, Amazon EFA), and containers technologies (Docker, Enroot).
- Capability in supervising and improving substantial distributed training operations using PyTorch (DDP, FSDP), NeMo, or JAX. Moreover, an in-depth understanding of AI/ML workflows, involving data processing, model training, and inference pipelines.
- Proficiency in programming & scripting languages such as Python, Go, Bash, as well as familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) in addition to experience with parallel computing frameworks and paradigms.
- Dedication to ongoing learning and staying updated on new technologies and innovative methods in the AI/ML infrastructure sector.
- Excellent communication and collaboration skills, with the ability to work effectively with teams and individuals of different backgrounds.
NVIDIA offers competitive salaries and a comprehensive benefits package. Our engineering teams are growing rapidly due to outstanding expansion. If you're a passionate and independent engineer with a love for technology, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until May 1, 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.
Tips for Finding AI ML Engineering Jobs at NVIDIA
Tailor your portfolio to NVIDIA's stack
NVIDIA's AI ML Engineering roles consistently require hands-on experience with CUDA, TensorRT, and distributed training frameworks. Document projects involving GPU optimization or large model inference before applying, not after you land an interview.
Target teams building production AI systems
NVIDIA hires ML engineers into distinct verticals: autonomous vehicles, cloud AI, and developer tools. Applying to a team whose product aligns with your domain significantly improves your chances of clearing the technical screen.
Confirm your visa type before the offer stage
NVIDIA sponsors both H-1B and E-3 visas for this role. If you're an Australian citizen, raising the E-3 pathway early avoids delays, since E-3 doesn't require lottery selection and can be processed on a faster timeline.
Prepare for the specialty occupation standard early
USCIS requires H-1B petitions to demonstrate the role qualifies as a specialty occupation. For AI ML Engineering, that means your degree field and job duties need to align precisely. Gather transcripts and any graduate research documentation before your employer files.
Understand NVIDIA's internal immigration timeline
Large technology employers typically begin H-1B cap-subject filings in March for an October start date. If you're interviewing in Q4 or Q1, factor this window into your offer negotiation so your start date aligns with USCIS processing.
Use Migrate Mate to filter open roles by visa type
NVIDIA posts AI ML Engineering positions across multiple teams simultaneously. Use Migrate Mate to filter live openings specifically by visa sponsorship type, so you apply to roles where your visa category is already confirmed as supported.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for AI ML Engineers?
Yes, NVIDIA sponsors H-1B visas for AI ML Engineering roles. For cap-subject candidates, NVIDIA files petitions in the annual H-1B lottery window, which opens in March. If you're already in H-1B status with another employer, NVIDIA can file a transfer petition outside the lottery, which avoids the wait.
Which visa types does NVIDIA sponsor for AI ML Engineering roles?
NVIDIA sponsors H-1B visas for most international candidates in AI ML Engineering. Australian citizens can pursue the E-3 visa instead, which has no annual lottery and is generally faster to obtain. For candidates on a longer-term path, NVIDIA also supports EB-2 and EB-3 Green Card sponsorship once you're established in the role.
How do I apply for AI ML Engineering jobs at NVIDIA?
Applications go through NVIDIA's careers portal. Most AI ML Engineering roles require a technical screen covering GPU programming, model optimization, or systems design, followed by multiple rounds of interviews. Migrate Mate aggregates NVIDIA's open AI ML Engineering positions filtered by visa sponsorship type, which makes it easier to identify the right roles before applying directly on NVIDIA's site.
What qualifications does NVIDIA look for in AI ML Engineering candidates?
NVIDIA typically expects a bachelor's or master's degree in computer science, electrical engineering, or a closely related field. Practical experience with CUDA, large-scale model training, and inference optimization carries significant weight. For H-1B purposes, your degree field needs to align with the specific role, so a degree in a tangential discipline may require additional documentation showing equivalency.
How long does the visa sponsorship process take when joining NVIDIA?
Timeline depends on your visa category. E-3 consular processing typically takes two to six weeks once your employer files the Labor Condition Application with DOL. H-1B transfers for candidates already in status can take two to four months under standard USCIS processing. Cap-subject H-1B candidates must wait for the October 1 fiscal year start date, meaning an offer signed in spring may not result in a start date until fall.