Robotics Learning Engineer

Alexander Chapman; California (MO); On-site; USD 140,000 - 200,000; Full time

Job summary

Alexander Chapman is partnering with a robotics company building and deploying humanoid robots in real factory environments. They seek a Simulation & Robot Learning Engineer to own the pipeline from high-fidelity simulation to policies that transfer onto physical robots.

In NYC on-site, you will lead simulation development, data generation, and policy deployment, with heavy focus on integrating Isaac Sim, MuJoCo, Gazebo, and related tools.

Qualifications

• 4+ years hands-on experience building simulation environments.
• Experience deploying learned policies on real robots.
• Deep experience with Isaac Sim, MuJoCo, Drake, Gazebo, SAPIEN, Genesis.

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++

Robotics

Reinforcement Learning

Imitation Learning

Simulation

Tools

Isaac Sim

MuJoCo

Gazebo

SAPIEN

Drake

Job description

Connect with me for more opportunities: https: //www.linkedin.com/in/arianit-krasniqi-b6ba66388/

I'm partnering with an ambitious robotics company building and deploying humanoid robots in real factory environments. They're looking for a Simulation & Robot Learning Engineer to own the pipeline from high-fidelity simulation and large-scale data generation to policies that reliably transfer onto physical robots.

In this role, you'll:

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

They're looking for someone with 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, or similar is important, along with 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.

This is an on-site opportunity in New York City with significant ownership over the simulation and learning stack and the chance to solve some of the hardest problems in getting learned robotic behavior to work reliably in the real world.