Director, Engineering - Autonomous Vehicles

NVIDIA•Us, CA

About The Position

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. At NVIDIA, we are building an advanced AI-native autonomous driving architecture. It integrates traditional safety-focused autonomous systems, fully learned driving approaches, foundation models, world models, and scalable AI systems into one production platform. Autonomous driving is entering a new era. The next generation of AV systems will not be purely classical robotics stacks, nor purely end-to-end neural networks. We believe the future lies in deeply integrating the strengths of classical, interpretable safety architectures with large-scale learning-based systems. We seek a Director-level technical leader who can define this architecture and build a world-class engineering organization. They will lead the team and drive technology from research and prototypes through production and scale. This leader will work across classical autonomy and modern AI. They will bring together teams and technologies in prediction, planning, control, safety, end-to-end driving, large-scale pre-trained models, and embodied AI. If you want to build the architecture that could define autonomous systems for the next decade, this is the place.

Requirements

PhD with 12+ years, MS with 10+ years, or BS (or equivalent experience) with 15+ overall years of relevant industry experience in Computer Science, Computer Engineering, Robotics, Machine Learning, or a related technical field.
8+ years of experience leading a team.
Significant technical leadership experience, including leading senior engineers, architects, and/or engineering managers working on sophisticated production systems.
Extensive knowledge of traditional driverless vehicle system designs, including prediction, planning, decision making, control, safety, redundancy, and fallback systems.
Solid grasp of modern learning-based autonomy, including end-to-end driving models, foundation models, large-scale machine learning systems, or embodied AI.
Experience driving end-to-end self-driving system builds and understanding interactions involving perception as well as planning and control.
Demonstrated ability to attract, recruit, mentor, and grow exceptional engineering talent.
Excellent interpersonal skills with a capacity to influence technical experts, executives, researchers, and cross-functional partners.

Nice To Haves

Experience building hybrid AV architectures combining classical safety systems with end-to-end learned driving.
Experience with VLA frameworks, global representations, foundational architectures, large-scale multimodal systems, or generative approaches to autonomous driving.
Experience with CUDA, GPU computing, and accelerated AI infrastructure.
A record of technical leadership through patents, publications, widely deployed systems, or significant contributions to driverless vehicle technology, robotics, or embodied AI.

Responsibilities

Set the technical vision and architecture for NVIDIA’s next-generation autonomous driving stack, spanning both classical and learning-based approaches.
Build and lead a high-performing organization of engineers and technical leaders working across Prediction, Decision Making, Planning, Control, and Safety.
Define how classical safety-critical autonomy and learned driving systems work together within a unified production architecture.
Drive the architecture for robust system-level safety, redundancy, fallback, and degraded-mode strategies.
Lead the development and productionization of end-to-end, data-driven autonomous driving pipelines, from perception and reasoning through trajectory building and vehicle operation.
Advance large-scale vision-language-action (VLA) / driving foundation models and their integration into production autonomous vehicles.
Partner closely with research teams to translate breakthroughs in robotics, embodied AI, foundation models, and generative AI into production self-driving technology.