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 JobsINTRODUCTION
For more than 25 years, NVIDIA has been driving innovation in computer graphics, PC gaming, and accelerated computing. It’s a distinctive heritage of creativity driven by excellent technology—and outstanding people. Today, we’re harnessing the boundless capabilities of AI to build the next era of computing. An era where our GPU serves as the intelligence behind computers, robots, and autonomous vehicles that perceive the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be part of a varied, encouraging environment where everyone is motivated to perform at their highest level. Join our team and discover how you can build a lasting impact on the world.
NVIDIA's Metropolis team advances physical AI by building intelligent video analytics and perception solutions for smart cities, industrial automation, and autonomous systems at scale. We seek a Senior ML Engineer to compose and deliver next-generation Metropolis solutions powered by Cosmos, NVIDIA's world foundation model platform. Your main task will be constructing production-quality AI capabilities for Metropolis by demonstrating Cosmos to address real-world perception and physical AI challenges. You will also partner closely with the Cosmos team to develop and broaden the platform’s features aligned with the Metropolis product roadmap. If you are driven to push generative AI, simulation, and large-scale model development forward and want your work to impact real-world systems, we invite you to apply.
ROLE AND RESPONSIBILITIES
What you'll be doing:
- Build and deliver innovative Metropolis AI solutions powered by Cosmos world foundation models, addressing real-world requirements in intelligent video analytics, perception, and physical AI.
- Find areas where Cosmos models underperform or lack capability and recommend new solutions. These involve generating synthetic data, refining tuning methods, and improving architecture. Metropolis use cases serve as the main reference.
- Partner with the Cosmos team to identify and prioritize new platform features guided by the Metropolis product roadmap, contributing to deliverables that support the wider ecosystem.
- Lead the open-sourcing of solutions and research artifacts developed by the team, contributing to the broader AI and research community.
- Stay current with the latest advances in foundation models, generative architectures, and training methodologies, and actively bring relevant insights back to the team.
- Partner multi-functionally with Product, Program, Engineering, and Data Procurement teams to drive alignment and unblock execution.
BASIC QUALIFICATIONS
What we need to see:
- MSc or PhD in Computer Science, Electrical Engineering, or a related field — or equivalent experience.
- 8+ years of proven experience in applied machine learning or AI research.
- Deep expertise in deep learning fundamentals, with hands-on experience in diffusion models and generative architectures.
- Experience in pre-training or refining large language models (LLMs), vision-language models (VLMs), or world foundation models (WFMs).
- Experience working with large-scale foundation models, including training workflows, fine-tuning techniques, and evaluation approaches.
- Experience working with simulation environments like Isaac Sim or similar platforms.
- Consistent track record of leading projects end-to-end and delivering results with clarity and accountability.
- Ability to manage and complete tasks across multiple parallel workstreams in a fast-paced, evolving environment.
- End-to-end understanding of ML development and deployment life cycle with the ability to quickly adopt and bring to bear modern AI development tools and workflows.
PREFERRED QUALIFICATIONS
Ways to stand out from the crowd:
- Hands-on experience with model compression, quantization, and real-time inference optimization for production deployments.
- Previous experience implementing AI solutions in physical settings such as public areas, smart infrastructure, or robotics platforms.
- Experience scaling AI systems across distributed infrastructure, including multi-node training and large-scale data pipelines.
- Published research or open-source contributions in relevant areas such as generative models, synthetic data, or physical AI.
- Familiarity with CUDA, Triton, or low-level GPU kernel development for inference pipeline acceleration.
COMPENSATION
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 August 21, 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
For more than 25 years, NVIDIA has been driving innovation in computer graphics, PC gaming, and accelerated computing. It’s a distinctive heritage of creativity driven by excellent technology—and outstanding people. Today, we’re harnessing the boundless capabilities of AI to build the next era of computing. An era where our GPU serves as the intelligence behind computers, robots, and autonomous vehicles that perceive the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be part of a varied, encouraging environment where everyone is motivated to perform at their highest level. Join our team and discover how you can build a lasting impact on the world.
NVIDIA's Metropolis team advances physical AI by building intelligent video analytics and perception solutions for smart cities, industrial automation, and autonomous systems at scale. We seek a Senior ML Engineer to compose and deliver next-generation Metropolis solutions powered by Cosmos, NVIDIA's world foundation model platform. Your main task will be constructing production-quality AI capabilities for Metropolis by demonstrating Cosmos to address real-world perception and physical AI challenges. You will also partner closely with the Cosmos team to develop and broaden the platform’s features aligned with the Metropolis product roadmap. If you are driven to push generative AI, simulation, and large-scale model development forward and want your work to impact real-world systems, we invite you to apply.
ROLE AND RESPONSIBILITIES
What you'll be doing:
- Build and deliver innovative Metropolis AI solutions powered by Cosmos world foundation models, addressing real-world requirements in intelligent video analytics, perception, and physical AI.
- Find areas where Cosmos models underperform or lack capability and recommend new solutions. These involve generating synthetic data, refining tuning methods, and improving architecture. Metropolis use cases serve as the main reference.
- Partner with the Cosmos team to identify and prioritize new platform features guided by the Metropolis product roadmap, contributing to deliverables that support the wider ecosystem.
- Lead the open-sourcing of solutions and research artifacts developed by the team, contributing to the broader AI and research community.
- Stay current with the latest advances in foundation models, generative architectures, and training methodologies, and actively bring relevant insights back to the team.
- Partner multi-functionally with Product, Program, Engineering, and Data Procurement teams to drive alignment and unblock execution.
BASIC QUALIFICATIONS
What we need to see:
- MSc or PhD in Computer Science, Electrical Engineering, or a related field — or equivalent experience.
- 8+ years of proven experience in applied machine learning or AI research.
- Deep expertise in deep learning fundamentals, with hands-on experience in diffusion models and generative architectures.
- Experience in pre-training or refining large language models (LLMs), vision-language models (VLMs), or world foundation models (WFMs).
- Experience working with large-scale foundation models, including training workflows, fine-tuning techniques, and evaluation approaches.
- Experience working with simulation environments like Isaac Sim or similar platforms.
- Consistent track record of leading projects end-to-end and delivering results with clarity and accountability.
- Ability to manage and complete tasks across multiple parallel workstreams in a fast-paced, evolving environment.
- End-to-end understanding of ML development and deployment life cycle with the ability to quickly adopt and bring to bear modern AI development tools and workflows.
PREFERRED QUALIFICATIONS
Ways to stand out from the crowd:
- Hands-on experience with model compression, quantization, and real-time inference optimization for production deployments.
- Previous experience implementing AI solutions in physical settings such as public areas, smart infrastructure, or robotics platforms.
- Experience scaling AI systems across distributed infrastructure, including multi-node training and large-scale data pipelines.
- Published research or open-source contributions in relevant areas such as generative models, synthetic data, or physical AI.
- Familiarity with CUDA, Triton, or low-level GPU kernel development for inference pipeline acceleration.
COMPENSATION
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 August 21, 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 AI ML Engineering Jobs at NVIDIA
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Get Access To All JobsTips 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.