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NVIDIA

Designs the chips and software that power AI

Director – Engineering - Autonomous Vehicles

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • PhD with 12 or more years, MS with 10 or more years, or BS (or equivalent experience) with 15 or more years of relevant industry experience in Computer Science, Computer Engineering, Robotics, Machine Learning, or a related technical field.
  • At least 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.
  • A 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, planning, and control.
  • Demonstrated ability to attract, recruit, mentor, and grow exceptional engineering talent.

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 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.

Desired 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.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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