Robotics Learning Engineer

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

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.