Director of Engineering, End to End Autonomous Driving

NVIDIA•Santa Clara, CA

About The Position

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!

Requirements

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.

Nice To Haves

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.

Responsibilities

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.