Simulation & Robot Learning Engineer

Member of Technical Staff — Simulation & Robot Learning Engineer

Location: New York, NY — Onsite
Employment Type: Full-time
Team: Robot Learning / Software

We are building and deploying humanoid robots designed to operate on real factory floors and perform meaningful work in demanding production environments. Simulation is a critical part of making these systems deployment‑ready quickly, allowing us to develop, train, evaluate, and iterate on robotic behaviors at scale.

We are looking for a Member of Technical Staff (Simulation & Robot Learning Engineer) to build high‑fidelity simulation environments, develop scalable data‑generation pipelines, train robot‑learning policies, and close the sim‑to‑real gap so that behaviors developed in simulation translate reliably to physical robots.

As a Member of Technical Staff (Simulation & Robot Learning Engineer), you will own the pipeline from simulation environments and task authoring through data generation, policy training, evaluation, and deployment. You will work across reinforcement learning, imitation and diffusion policies, Vision‑Language‑Action models, world models, and emerging agentic AI techniques.

This is a hands‑on individual contributor position for a Member of Technical Staff (Simulation & Robot Learning Engineer) who enjoys working across simulation, machine learning, robotics, and physical hardware. You should be comfortable building environments from scratch, tuning physics and contact models, scaling training infrastructure, and debugging the difficult gap between simulation and reality.

You will work on challenging manipulation problems involving rigid, articulated, and deformable objects, across single‑arm, bimanual, whole‑body, and wheeled humanoid platforms.

• Build and maintain high‑fidelity simulation environments using platforms such as Isaac Sim/Lab, Mu Jo Co , Drake, Gazebo, SAPIEN, Genesis, or similar tools.
• Author scenes, assets, physics configurations, contact models, and task definitions for robotic manipulation.
• Train deployable robot policies using reinforcement learning, imitation learning, diffusion policies, VLAs, world models, and related approaches.
• Drive policies from simulation onto physical robots and improve their robustness in real‑world operation.
• Close the sim‑to‑real gap using domain randomization, system identification, real‑to‑sim calibration, physics tuning, and systematic hardware debugging.
• Build scalable pipelines for synthetic data generation, teleoperation and demonstration collection, procedural scene and task generation, and automated training curricula.
• Use agentic AI and LLM‑based tooling to automate simulation and data workflows, including environment generation, task creation, reward design, randomization configuration, evaluation, and curriculum generation.
• Develop systems that allow simulation and learning experiments to iterate in hours rather than weeks.
• Work on challenging manipulation regimes involving rigid, articulated, and deformable objects such as cloth, cables, and soft materials.
• Build evaluation and benchmarking infrastructure that makes simulation performance a trustworthy indicator of real‑world robot performance.
• Work closely with controls, perception, mechanical, and other learning engineers to translate target behaviors into smooth, safe, and repeatable robot motion.
• Help establish scalable infrastructure and engineering practices for robot learning and simulation.

• BS, MS, or PhD in Robotics, Computer Science, Machine Learning, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or a related technical field.
• 4+ years of hands‑on experience building simulation environments and training or deploying robot‑learning…