Planning - Technical Lead

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

Lead and mentor a team developing motion planning modules for autonomous vehicles, integrating ML components, perception outputs, and map data for dynamic path planning. Requires C++ and experience with production software teams in planning algorithms.

What you'd actually do

  1. Lead and mentor a team of software engineers focused on onroad behavior software and motion planning for autonomous navigation
  2. Develop state-of-the-art onroad behavior software and leverage ML components to achieve highway and city driving
  3. Leverage lightweight map data (SD maps) and the outputs of a perception system to determine driving costs to use for planning
  4. Architect and oversee the implementation of planning modules that integrate perception outputs, predictions, and lightweight map data for dynamic path planning
  5. Ensure deployment readiness by establishing testing protocols and validation on production hardware and real-world road conditions, partnering with evaluation and vehicle teams

Skills

Required

  • BS in Computer Science, Robotics, Applied Mathematics, or related engineering field with focus on planning
  • 5+ years of experience building and leading production software teams, specifically in planning algorithms for autonomous vehicles
  • Proven ability to lead technical projects end-to-end and mentor junior engineers
  • Fluency in modern C++ and experience with performance-critical software
  • Strong problem-solving skills and ability to deliver practical, scalable solutions in fast-paced environments
  • Excellent communication skills for both technical leadership and customer-facing interactions

Nice to have

  • Advanced degree (MSc or PhD) in Computer Science, Robotics, Applied Mathematics, or related engineering field with focus on planning
  • Experience with safety-critical software development and automotive industry standards
  • Hands-on experience testing planning systems on autonomous vehicles
  • Familiarity with machine learning applications in motion planning, combining traditional approaches with the state of the art

What the JD emphasized

  • planning algorithms for autonomous vehicles
  • production software teams

Other signals

  • autonomous vehicles
  • motion planning
  • ML components