Robotics Learning Engineer

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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.

• 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, Mu Jo Co , 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.