Senior Autonomous Driving Software Engineer, L4 Planning

NVIDIA NVIDIA · Semiconductors · Shanghai, China +2

Senior engineer to build the main stack for autonomous driving, focusing on prediction, decision-making, planning, and control architecture. This includes crafting system-level safety, implementing end-to-end data-driven AV pipelines, large-scale model inference, and integrating classical and end-to-end hybrid systems for scalability from research to production. Requires 6+ years of experience in production autonomous driving systems, AI foundation models, large-scale ML systems, end-to-end driving models, and robotics/embodied AI system architecture.

What you'd actually do

  1. Building the main stack for autonomous driving, concentrating on prediction, decision-making, planning, and control architecture.
  2. Crafting robust system-level safety and fallback strategies within classical safety stacks.
  3. Implementing end-to-end data-driven AV pipelines and large-scale model inference architectures.
  4. Integrating classical and end-to-end hybrid systems, ensuring seamless scalability from research to production.
  5. Collaborating with a world-class team of engineers to build a unified production platform that sets new standards in the industry.

Skills

Required

  • BS or higher in an engineering or technical field or equivalent work experience
  • At least 6 years of hands-on experience in developing production autonomous driving systems
  • Proficiency in forecasting, scheduling, and control frameworks for autonomous vehicles
  • Experience with AI foundation models, large-scale ML systems, and end-to-end driving models
  • A strong background in robotics or embodied AI systems and system architecture from concept to scale

Nice to have

  • deep technical expertise in autonomy architecture
  • ML systems
  • robotics platforms
  • solving complex real-world problems
  • ambitious

What the JD emphasized

  • At least 6 years of hands-on experience in developing production autonomous driving systems.
  • A strong background in robotics or embodied AI systems and system architecture from concept to scale.
  • proven track record of developing flawless, world-class systems

Other signals

  • building end-to-end data-driven AV pipelines
  • large-scale model inference architectures
  • integrating classical and end-to-end hybrid systems
  • experience with AI foundation models, large-scale ML systems, and end-to-end driving models
  • strong background in robotics or embodied AI systems and system architecture from concept to scale