Senior Senior Simulation Engineer - Core Simulation Platform

Apptronik
Sunnyvale, CA / MV, Mountain View, California, United States2026-06-17

About the job

At the core of every robot policy we ship is a simulation platform that has to be fast enough for RL training, accurate enough for controls validation, and stable enough to be the team’s daily driver. As Staff Simulation Engineer for Core Simulation Platform, you’ll contribute to the architecture and development of that platform alongside senior team members, applying strong C++ and Python skills, robotics fundamentals, and modern simulator experience to keep the platform robust, performant, and parallel.

Responsibilities

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.

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.

Improve sim-to-real transfer through advanced contact models and actuator dynamics that help policies trained in simulation transfer to physical humanoid hardware.

Develop sensor-realistic environments (camera, LiDAR, depth) that challenge the perception stack with dynamic and diverse worlds.

Ship platform improvements that visibly reduce iteration time for RL, controls, and perception teams.

Work with Controls, AI Research, and Perception teams to support their evolving simulation needs as the platform grows.

Qualifications

Minimum

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)

Preferred

No preferred qualifications listed.