Senior Senior Simulation Engineer - Core Simulation Platform

Apptronik Apptronik · Robotics · Mountain View, CA · Software Engineering

Apptronik is seeking a Senior Simulation Engineer to develop and scale their core simulation platform for AI-powered robots. This role involves building high-performance digital twins for RL training, controls validation, and CI/CD, with a focus on improving sim-to-real transfer and supporting AI research and perception teams. Requires strong C++/Python, robotics fundamentals, experience with modern simulators, and large-scale training workloads.

What you'd actually do

  1. Contribute to the architecture and development of the core simulation platform — high-performance digital twins that serve as the foundation for policy training, controls validation, and CI/CD integration testing.
  2. Help build and scale cloud-native simulation pipelines capable of generating millions of experience hours per day, parallelizing physics and rendering for rapid policy iteration.
  3. Improve sim-to-real transfer through advanced contact models and actuator dynamics that help policies trained in simulation transfer to physical humanoid hardware.
  4. Develop sensor-realistic environments (camera, LiDAR, depth) that challenge the perception stack with dynamic and diverse worlds.
  5. Ship platform improvements that visibly reduce iteration time for RL, controls, and perception teams.

Skills

Required

  • Excellent C++ and/or Python programming skills
  • Familiar with software development best practices: CI/CD, automated testing, and code quality standards
  • Understanding of robotics concepts: kinematics, dynamics, controls, system identification
  • Experience with modern robotic simulators (Isaac Lab, MuJoCo, or equivalent)
  • Understanding of Reinforcement Learning with research or production experience
  • Experience with large-scale training workloads — deploying parallel simulations on cloud platforms (AWS, GCP, Azure) with distributed computing frameworks (e.g., Ray, Kubernetes)

What the JD emphasized

  • RL training
  • policy iteration
  • sim-to-real transfer
  • RL
  • controls
  • perception

Other signals

  • simulation platform for RL training
  • high-performance digital twins
  • cloud-native simulation pipelines
  • sim-to-real transfer
  • large-scale training workloads