At NVIDIA, we are seeking a visionary leader to join our autonomous driving team! You will lead the design and deployment of cutting-edge end-to-end autonomous systems running on NVIDIA chips in mass-production vehicles. Our strategy has evolved from AI 1.0 to AI 2.0—teaching an intelligent agent to drive. This next phase leverages LLMs, VLMs, and VLAs to bring unprecedented reasoning and planning capabilities to autonomous vehicles and general robotics. Let’s lead the future of autonomy—together!
What You’ll Be Doing:
Strategic Leadership: Define the technical roadmap for large-scale generative, imitation, and reinforcement learning models to advance vehicle planning and reasoning.
Team Management: Recruit, mentor, and lead an extraordinary team of ML engineers passionate about building and fine-tuning LLM/VLM/VLA systems for real-world robotics.
Execution & Planning: Oversee tactical execution of data generation and collection strategies to ensure the highest quality training datasets for production.
Cross-functional Collaboration: Partner with hardware, firmware, and safety teams to deploy AI models in production environments, ensuring they meet rigorous performance and safety standards.
Technical Oversight: Provide deep technical mentorship on integrating ML models into the rest of the autonomous driving stack to build production-quality, safety-critical software.
What We Need to See:
Production Experience: Hands-on experience delivering pioneering ML planning models at scale in real-world environments. Strong understanding of the full lifecycle from research to vehicle deployment.
Proven Leadership: 5+ years of experience managing high-performing ML teams with a focus on autonomous systems, robotics, or computer vision.
Technical Mastery: Deep understanding of modern deep learning architectures (LLMs, VLMs, or VLAs) and optimization techniques for large-scale training.
Product Delivery: A track record of shipping production-grade ML models at scale for safety-critical applications.
Strategic Vision: Ability to translate complex research into tactical engineering plans and long-term product roadmaps.
Academic Background: Master’s degree or PhD in CS, EE, or a related field (or equivalent experience).
Industry Experience: 12+ overall years of professional experience in the AV or AI industry.
Ways to Stand Out from the Crowd:
Experience scaling LLM/VLM/VLA systems specifically for embodied AI or real-time robotics.
Publications, open-source contributions, or competition wins related to LLM/VLM/VLA systems.
Success in managing multi-site teams and navigating the complexities of mass-production vehicle launches.
Deep expertise in behavior and motion planning within resource-constrained environments.
A strong track record of building large-scale data flywheels and training infrastructure and a background in optimizing high-performance algorithms for real-time deployment on NVIDIA hardware.
You will also be eligible for equity and benefits.
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.Skills Required
- Production experience in delivering ML planning models at scale
- 5+ years managing high-performing ML teams
- Deep understanding of deep learning architectures (LLMs, VLMs, VLAs)
- Track record of shipping production-grade ML models
- Master's degree or PhD in CS, EE, or related field
- 12+ years of professional experience in AV or AI industry
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
