Sensor Sim - ML Engineer

Applied Intuition Applied Intuition · Robotics · Sunnyvale, CA · Autonomy Tooling Software Engineering

This role involves applying machine learning techniques, including generative models and agentic systems, to production-grade sensor simulation for autonomous systems in industries like automotive and defense. The engineer will work on modeling worlds and sensors (Lidar, Radar, Camera) and drive the development and deployment of these ML approaches.

What you'd actually do

  1. Drive research, development and deployment of generative and other machine learning techniques into our sensor simulator.
  2. Work closely with rendering and physics-based modeling teams to build novel approaches fusing the best of all techniques.
  3. Focus on projects with a clear path to production value and customer impact.

Skills

Required

  • Bachelor's degree in Computer Science, Software Engineering, or equivalent
  • 5+ years of experience building software components or (sub) systems that address real-world machine learning challenges
  • Hands-on experience with evaluation and optimization for large generative models
  • Hands-on experience with agentic system design
  • Deep understanding of machine learning foundations
  • Understanding of 3D geometry, optical flow or video generation

Nice to have

  • Experience working with Generative World Models (Cosmos, UniSim, sora-style architectures)
  • Experience with robotics simulation products (IssacSim, MuJoCo, Omniverse, USD)
  • Experience with applying synthetic data to machine learning tasks
  • Hands on experience with characterization of models for Lidar, Radar, and Camera

What the JD emphasized

  • 5+ years of experience building software components or (sub) systems that address real-world machine learning challenges
  • Hands-on experience with evaluation and optimization for large generative models
  • Hands-on experience with agentic system design
  • Deep understanding of machine learning foundations who can apply various techniques to new problems

Other signals

  • incorporation of modern machine learning approaches into production-grade sensor simulation
  • apply state of the art approaches to model worlds and sensors such as Lidars, Radars and Cameras
  • Drive research, development and deployment of generative and other machine learning techniques into our sensor simulator
  • Focus on projects with a clear path to production value and customer impact