Staff Software Engineer, Deep Learning Acceleration

Aurora Innovation Aurora Innovation · Robotics · Mountain View, CA · Software Autonomy Sensing

Staff Software Engineer focused on Deep Learning Acceleration for Aurora's Autonomous Vehicle (AV) systems. Responsibilities include performance analysis and optimization of deep learning networks for both onboard vehicle deployment and large-scale data center training, troubleshooting performance issues using profiling and roofline model techniques, and collaborating with cross-functional teams to enhance self-driving technology efficiency.

What you'd actually do

  1. Conduct performance analysis and optimization of Deep Learning networks running on the Autonomous Vehicle (AV).
  2. Optimize software architecture, system performance, and latency for deep learning applications.
  3. Work on deployment of deep learning models on the AV and training on large-scale data centers.
  4. Troubleshoot performance issues using profiling and roofline model techniques.
  5. Collaborate with cross-functional teams to enhance the efficiency of self-driving technology.

Skills

Required

  • CUDA
  • C++
  • Python
  • high-performance computing
  • parallel programming
  • GPU memory usage optimization
  • latency minimization
  • throughput maximization
  • NVIDIA Nsight Systems
  • Nsight Compute
  • roofline model
  • PyTorch
  • TensorFlow
  • computer vision
  • transformer-based deep learning architectures
  • neural network building blocks
  • performance bottleneck diagnosis
  • Linux/Unix environments

Nice to have

  • motion planning
  • robotics
  • autonomous systems
  • systems software
  • TensorRT
  • OpenAI Triton
  • Mojo
  • inference acceleration tools

What the JD emphasized

  • performance analysis and optimization
  • software architecture, system performance, and latency
  • deployment of deep learning models
  • training on large-scale data centers
  • Troubleshoot performance issues
  • profiling and roofline model techniques

Other signals

  • performance optimization
  • deep learning acceleration
  • onboard vehicle deployment
  • large-scale data center training