Senior Perception Engineer - Autonomous Vehicles

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

Senior Perception Engineer at NVIDIA focused on developing and productizing autonomous driving solutions using deep learning and multi-sensor fusion. The role involves applied research, algorithm development, and ensuring solutions meet production requirements for safety, latency, and robustness.

What you'd actually do

  1. Perception experts with application focus will be on multi-sensor fusion based deep learning model development for obstacle perception/fusion in complex driving environments.
  2. Applied research and development of innovative deep learning and multi-sensor fusion algorithms to improve output accuracy of 3D obstacle perception solutions under challenging and diverse scenarios.
  3. Identify and analyze the strength and weakness of the developed 3D obstacle perception solutions using large scale benchmark data (both real and synthetic) and improve them iteratively through KPI building and optimization. This includes careful data verification, model architecture design, understanding details of loss function engineering, and being capable of finding detailed ML bugs and iterating toward perfection.
  4. Productize the developed 3D obstacle perception solutions by meeting product requirements for safety, latency, and SW robustness, with a strong emphasis on production deep learning model development.
  5. Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, so that value of data is maximized.

Skills

Required

  • deep learning
  • multi-sensor fusion
  • PyTorch
  • Python
  • C++
  • data verification
  • model architecture design
  • loss function engineering
  • ML bug debugging

Nice to have

  • end-to-end deep learning model development
  • autonomous driving perception
  • robotics perception
  • DNN-based solutions to embedded platforms
  • real time applications
  • 3D computer vision
  • camera calibrations
  • sensor fusion principles
  • CUDA
  • CUDA kernels

What the JD emphasized

  • 10+ years of hands-on work experience
  • production deep learning model development
  • deep learning frameworks (e.g., PyTorch)
  • multi-sensor fusion (cameras, ultrasonic sensors, radar)
  • BS/MS/PhD in CS, EE, sciences or related fields (or equivalent experience)

Other signals

  • deep learning
  • autonomous driving
  • multi-sensor fusion
  • production deep learning model development