Description
NVIDIA is seeking a Director-level technical leader to define the architecture for its next-generation autonomous driving stack. The successful candidate will lead a high-performing organization of engineers and technical leaders working on Prediction, Decision Making, Planning, Control, and Safety.
The Director will set the technical vision and architecture for NVIDIA's next-generation autonomous driving stack, spanning both classical and learning-based approaches. They will build and lead a team, define how classical safety-critical autonomy and learned driving systems work together, and drive the architecture for robust system-level safety.
Key responsibilities include:
- Setting the technical vision and architecture for NVIDIA's next-generation autonomous driving stack
- Building and leading a high-performing organization of engineers and technical leaders
- Defining how classical safety-critical autonomy and learned driving systems work together
- Driving the architecture for robust system-level safety, redundancy, fallback, and degraded-mode strategies
- Leading the development and productionization of end-to-end, data-driven autonomous driving pipelines
- Advancing large-scale vision-language-action (VLA) / driving foundation models and their integration into production autonomous vehicles
- Partnering closely with research teams to translate breakthroughs in robotics, embodied AI, foundation models, and generative AI into production self-driving technology
The ideal candidate will have:
- 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 and modern learning-based autonomy
- Experience driving end-to-end self-driving system builds and understanding interactions involving perception, planning, and control
- Demonstrated ability to attract, recruit, mentor, and grow exceptional engineering talent
Preferred qualifications include experience building hybrid AV architectures, VLA frameworks, and large-scale multimodal systems.