Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 4
CUDA @ 4
GPU @ 4
GenAI
Generative AI
Leadership @ 4
Machine Learning @ 8
Robotics @ 8
Technical Leadership @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
NVIDIA is building an advanced AI-native autonomous driving architecture that integrates traditional safety-focused autonomous systems, fully learned driving approaches, foundation models, world models, and scalable AI systems into one production platform. The role will define this architecture and build a world-class engineering organization, driving technology from research and prototypes through production and scale across classical autonomy and modern AI.
Responsibilities
- Set the technical vision and architecture for NVIDIA's next-generation autonomous driving stack, spanning 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) and driving foundation models and integrate them into production autonomous vehicles.
- Partner with research teams to translate breakthroughs in robotics, embodied AI, foundation models, and generative AI into production self-driving technology.
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 understanding 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, planning, and control.
- Demonstrated ability to attract, recruit, mentor, and grow exceptional engineering talent.
- Excellent interpersonal skills, with the ability to influence technical experts, executives, researchers, and cross-functional partners.
Preferred Qualifications
- Experience building hybrid autonomous vehicle 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.
Compensation and Benefits
The base salary range is USD 320,000 to USD 488,750, determined based on location, experience, and the pay of employees in similar positions. The role is also eligible for equity and benefits.
NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.