Robotics Simulation & Learning Engineer — Sim-to-Real Expert

Alexander Chapman; California (MO); On-site; USD 120,000 - 180,000; Full time

Job summary

Alexander Chapman partners with a robotics company seeking a Simulation & Robot Learning Engineer to own the pipeline from high-fidelity simulation and large-scale data generation to policies that transfer onto physical robots.

In this on-site role in New York City, you will build and tune simulation environments, train robot policies using RL and imitation learning, and push sim-to-real transfer through domain randomization and calibration.

Qualifications

• 4+ years of hands-on experience building simulation environments and deploying learned policies on real robots.
• Deep experience with Isaac Sim, MuJoCo, Drake, Gazebo, SAPIEN, Genesis is important.
• Strong Python, PyTorch or JAX, and enough C++ to work close to the system.
• Experience with manipulation, deformable objects, bimanual systems, humanoids, GPU-accelerated simulation, or VLA/world models is a strong plus.

Responsibilities

• Build and tune simulation environments, assets, physics, scenes, and manipulation tasks
• Train robot policies using RL, imitation learning, diffusion policies, VLAs, and/or world models
• Close the sim-to-real gap through domain randomization, system identification, calibration, and real-world debugging
• Build scalable synthetic data, teleoperation, procedural generation, and automated curriculum pipelines
• Use agentic AI to automate environment, task, reward, evaluation, and data generation
• Develop benchmarking infrastructure that makes simulation performance predictive of hardware performance

Skills

Python

PyTorch

JAX

C++

Reinforcement Learning

Imitation Learning

Tools

Isaac Sim

MuJoCo

Drake

Gazebo

SAPIEN

Genesis

Job description

Alexander Chapman partners with a robotics company seeking a Simulation & Robot Learning Engineer to own the pipeline from high-fidelity simulation and large-scale data generation to policies that transfer onto physical robots.

In this on-site role in New York City, you will build and tune simulation environments, train robot policies using RL and imitation learning, and push sim-to-real transfer through domain randomization and calibration.