Director of Engineering, End-to-End Autonomous Driving

Details

Join NVIDIA's autonomous driving team to lead the design and deployment of end-to-end autonomous systems running on NVIDIA chips for mass-production vehicles. This role focuses on advancing vehicle planning and reasoning using modern AI techniques (LLMs, VLMs, VLAs) and applying them to safety-critical, real-world robotics and AV deployments.

Responsibilities

• Define the technical roadmap for large-scale generative, imitation, and reinforcement learning models to advance vehicle planning and reasoning.
• Recruit, mentor, and lead a team of ML engineers focused on building and fine-tuning LLM/VLM/VLA systems for real-world robotics.
• Oversee tactical execution of data generation and collection strategies to ensure high-quality training datasets for production.
• Partner with hardware, firmware, and safety teams to deploy AI models in production environments meeting rigorous performance and safety standards.
• Provide deep technical mentorship on integrating ML models into the autonomous driving stack to build production-quality, safety-critical software.

Requirements

• Hands-on production experience delivering ML planning models at scale in real-world environments, with strong understanding of the full lifecycle from research to vehicle deployment.
• 5+ years managing high-performing ML teams with a focus on autonomous systems, robotics, or computer vision.
• Deep understanding of modern deep learning architectures (LLMs, VLMs, or VLAs) and optimization techniques for large-scale training.
• Track record of shipping production-grade ML models at scale for safety-critical applications.
• Ability to translate complex research into tactical engineering plans and long-term product roadmaps.
• Master's degree or PhD in CS, EE, or a related field (or equivalent experience).
• 12+ years of overall professional experience in the AV or AI industry.

Ways to Stand Out

• 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.
• Experience managing multi-site teams and navigating mass-production vehicle launches.
• Deep expertise in behavior and motion planning within resource-constrained environments.
• Strong track record building large-scale data flywheels, training infrastructure, and optimizing high-performance algorithms for real-time deployment on NVIDIA hardware.

Compensation & Other Details

• Base salary range: 320,000 USD - 488,750 USD (determined based on location, experience, and peer pay).
• Eligible for equity and benefits (link to NVIDIA benefits provided in original posting).
• Application acceptance at least until March 22, 2026.
• NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.