Software Engineer - Performance Optimization

Applied Intuition Applied Intuition · Robotics · Sunnyvale, CA · SDS Software Engineering

Software Engineer focused on optimizing application-layer software for embedded systems in autonomous driving. The role involves analyzing and optimizing compute usage, profiling performance on embedded targets, and collaborating with ML runtime optimization engineers to ensure efficient model inference execution within tight compute budgets and real-world operating conditions.

What you'd actually do

  1. Analyze runtime performance of the application layer and identify potential resource contentions
  2. Optimize compute usage to fit within embedded platform constraints without sacrificing algorithm accuracy or latency
  3. Profile and tune performance on embedded targets under real-world operating conditions
  4. Collaborate closely with ML runtime optimization engineers to ensure smooth model inference execution within the stack
  5. Deploy and validate production code on QNX, Linux-based embedded, or similar RTOS platforms

Skills

Required

  • 5+ years of experience in software development
  • Strong C++ development skills with a focus on runtime performance
  • Experience profiling CPU, GPU, and memory usage performance on constrained compute
  • Proven ability to debug complex runtime issues and resolve onboard resource contention

Nice to have

  • Exposure to ML models and runtime frameworks (PyTorch, ONNX, TensorRT)
  • Experience with memory-constrained deployments and concurrent scheduling
  • Prior experience with autonomous driving software stacks
  • Scripting experience for performance profiling and automation

What the JD emphasized

  • deep experience in optimizing the application-layer software for embedded systems
  • fitting a complex software stack into tight compute budgets
  • maintaining algorithmic performance
  • analyzing runtime behavior
  • ensuring efficient concurrent execution of multiple applications
  • ML runtime optimization engineers
  • smooth model inference execution

Other signals

  • optimizing the application-layer software for embedded systems
  • fitting a complex software stack into tight compute budgets
  • analyzing runtime behavior
  • efficient concurrent execution of multiple applications
  • ML runtime optimization engineers
  • smooth model inference execution