Engineering Manager, Prediction and Planning - Autonomous Vehicles

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Engineering Manager for NVIDIA's Autonomous Vehicles division, leading teams to build and scale AI-native autonomous driving systems, integrating classical safety stacks with foundation models and large-scale AI systems from research to production.

What you'd actually do

  1. Drive architecture development across Prediction / Planning / Decision / Control
  2. Guide the integration of AI-native driving systems, including VLA/VA models and World Model–based reasoning
  3. Ensure system adaptability through strong safety architecture and fallback strategies
  4. Lead development of scalable Classical × E2E hybrid autonomy stacks
  5. Drive cross-team integration across perception, planning, simulation, and infrastructure

Skills

Required

  • BS, MS or PhD in Computer Science, Robotics, or related engineering field (or equivalent experience)
  • Strong technical leader with experience building sophisticated autonomy or AI systems
  • 4+ years of experience managing a team
  • 8+ years of overall meaningful experience
  • Deep experience in self-driving vehicle technologies or robotics platforms
  • Strong understanding of Prediction / Planning / Control architectures
  • Experience with large-scale AI systems or ML infrastructure
  • Proven track record to drive complex technical programs across multiple teams
  • Experience building and scaling high-performing engineering teams

Nice to have

  • Successful track record of leading autonomous driving software releases or feature closure cycles from prototype to mass production
  • Experience with simulation-based validation, scenario management, and data-driven quality tracking frameworks
  • Deep knowledge of E2E AV software integration from perception through control, including dependencies, interface management, and performance tuning
  • Demonstrated ability to build, mentor, and scale technical leaders in a constantly evolving organization

What the JD emphasized

  • building and scaling the engineering organization
  • driving execution from research through production
  • systems scale from research prototypes to production deployment
  • build and grow a high-performing autonomy engineering team
  • successful candidates are able to balance long-term architecture vision with disciplined execution and delivery

Other signals

  • building and scaling the engineering organization
  • driving execution from research through production
  • systems scale from research prototypes to production deployment
  • build and grow a high-performing autonomy engineering team