Principal Soc Architect, Robotics and Automotive

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Principal SoC Architect role focused on defining and driving the hardware architecture for Edge AI, Robotics, and Autonomous Driving platforms, integrating GPUs, computer vision accelerators, and real-time processors. The role involves co-design with software and deep learning teams, PPA modeling, specification, validation, and post-silicon support, with a strong emphasis on real-time compute and safety standards.

What you'd actually do

  1. Define and drive the hardware architecture from early concept through bring-up and ecosystem enablement.
  2. Collaborate with software, deep learning, and safety teams to co-design hardware feature sets tailored for low-latency robotics spatial computing, sensor fusion, and autonomous driving pipelines.
  3. Perform rigorous performance, power, and area modeling, optimizing specifically for thermally constrained robotic enclosures and automotive ECUs.
  4. Author high-quality architecture specifications and drive the development of models to validate architectural choices.
  5. Define validation plans to ensure hardware meets strict real-time execution and safety metrics.

Skills

Required

  • 15+ years of deep architecture design experience in high-performance silicon
  • Top-level SoC definition
  • High-speed interfaces (PCIe, GMSL/Camera interfaces, Time-Sensitive Networking/TSN)
  • Advanced Subsystems (Multimedia/vision accelerator pipelines, CPU/GPU cache coherency, Virtualization, and Hardware Security)
  • Deterministic & Real-Time Compute (hardware support for RTOS, deterministic latency, mixed-criticality workloads)
  • Cross-Functional Technical Leadership
  • Master’s or PhD degree in Computer Engineering, Electrical Engineering, or equivalent experience

Nice to have

  • Functional safety experience
  • Mapping complex Edge AI workloads (e.g., transformer-based vision models, SLAM, or path planning) onto custom hardware architectures
  • SystemC, C++, or Python for architectural simulation and modeling

What the JD emphasized

  • low-latency robotics spatial computing
  • autonomous driving pipelines
  • real-time execution
  • safety metrics
  • functional safety
  • safety requirements
  • Edge AI workloads
  • custom hardware architectures

Other signals

  • Edge AI
  • Robotics
  • Autonomous Driving
  • SoC Architecture
  • Hardware Acceleration