Robotics Learning Engineer - Sim-to-Real Mastery

Alexander Chapman Ltd; California; On-site; USD 120,000 - 180,000; Full time

Job summary

Alexander Chapman Ltd. in New York City is seeking a Simulation & Robot Learning Engineer to own the pipeline from high-fidelity simulation to policies deployed on real robots.

You will lead the design and tuning of full simulation environments, physics, scenes, and manipulation tasks. You will train robot policies using RL, imitation learning, diffusion policies, VLAs, and/or world models, and work to close the sim-to-real gap through domain randomization, system identification, calibration,

Qualifications

• 4+ years hands-on experience building simulation environments and deploying learned policies.
• Experience with Isaac Sim, MuJoCo, Drake, Gazebo, SAPIEN, Genesis or similar.
• Strong Python, PyTorch or JAX, and C++ for near-system work.

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

Domain randomization

World models

Tools

Isaac Sim

MuJoCo

Drake

Gazebo

SAPIEN

Genesis

Job description

Alexander Chapman Ltd. in New York City is seeking a Simulation & Robot Learning Engineer to own the pipeline from high-fidelity simulation to policies deployed on real robots.

You will lead the design and tuning of full simulation environments, physics, scenes, and manipulation tasks. You will train robot policies using RL, imitation learning, diffusion policies, VLAs, and/or world models, and work to close the sim-to-real gap through domain randomization, system identification, calibration,