Simulation & Robot Learning Engineer

VeeAR Projects Inc.; New York (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

VeeAR Projects Inc. in New York seeks a robotics AI researcher to own the pipeline from environment to deployed behavior, building high-fidelity simulators and defining manipulation tasks for robotic platforms.

You will scale data generation, train deployable policies, and drive sim-to-real transfer. Collaborate with controls, perception, mechanical, and the learning team to turn target behaviors into safe, repeatable motion on arms and humanoids, using Isaac Sim/MuJoCo/Gazebo/SAPIEN.

Qualifications

• BS, MS, or PhD in Robotics, Computer Science, ML, EE, ME, or related field.
• 4+ years building simulation environments and training/deploying robot policies on real hardware.
• Deep experience with major robotics simulators and building/tuning environments.
• Demonstrated sim-to-real transfer with domain randomization and calibration.
• Strong grasp of RL and/or imitation/diffusion policies; familiarity with VLAs/world models.
• Experience scaling data generation for policy training and pipelines.

Responsibilities

• Build and maintain high-fidelity simulation environments and task definitions for manipulation.
• Train deployable robot policies using RL, imitation/diffusion, VLAs, or world models.
• Close the sim-to-real gap with domain randomization and system identification.
• Scale data generation including synthetic data, teleoperation, and curricula.
• Collaborate with controls, perception, mechanical, and learning teams to realize safe, repeatable motion.

Skills

Robotics simulation

Robot policy training

Domain randomization

Sim-to-real transfer

Python

C++ familiarity

Reinforcement learning

Imitation learning

World models

Education

BS, MS, or PhD in Robotics or related field

Tools

Isaac Sim/Lab

MuJoCo

Drake

Gazebo

SAPIEN

Genesis

Job description

You'll own the pipeline from environment to deployed behavior: authoring scenes and tasks, training policies (RL, imitation/diffusion, VLAs, or world models), scaling data collection and generation, and using agentic AI to automate the slow parts- scene and task generation, reward design, domain randomization, and evaluation- so the team iterates in hours, not weeks. You'll work across hard manipulation regimes, from rigid and articulated bodies to deformables (cloth, cable, soft objects), on platforms ranging from single cobot arms and bimanual setups to whole-body and wheeled humanoids.

What You'll Do

• Build and maintain high-fidelity simulation environments (Isaac Sim/Lab, MuJoCo, Drake, Gazebo, SAPIEN, or similar), scenes, assets, physics/contact tuning, and task definitions for manipulation.
• Train deployable robot policies: reinforcement learning, imitation/diffusion policies, VLAs, and/or world models, and drive them from sim to robust real-world behavior.
• Close the sim-to-real gap: domain randomization, system identification, real-to-sim calibration, and the messy debugging that makes a policy survive contact with reality.
• Scale data: build pipelines for large-scale synthetic data generation, teleop/demonstration data, procedural task and scene generation, and automated curricula.
• Use agentic AI to expedite the whole loop: auto-generating environments, tasks, reward functions, randomization configs, and evaluations, and turn slow manual work into fast automated iteration.
• Handle hard manipulation regimes: rigid, articulated, and deformable objects (cloth, cable, soft materials), and contact-rich tasks.
• Build evaluation and benchmarking infrastructure so that performance in sim is a trustworthy predictor of performance on hardware.
• Work closely with controls, perception, mechanical, and the rest of the learning team to turn a target behavior into smooth, safe, repeatable arm and whole-body motion on the robot.

Required Qualifications

• BS, MS, or PhD in Robotics, Computer Science, Machine Learning, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or a related field.
• 4+ years of hands-on experience building simulation environments and training/deploying robot policies on real hardware (an MS/PhD with equivalent research and project experience: roughly BS + 4 years, MS + 2 years, or a PhD in a relevant area).
• Deep experience with at least one major robotics simulator (Isaac Sim/Lab, MuJoCo, Drake, Gazebo, SAPIEN, Genesis, or similar), not just using it, but building and tuning environments in it.
• Demonstrated sim-to-real transfer: you've taken a policy trained in sim and made it work on a physical robot, and you understand why the gap exists and how to close it (domain randomization, system ID, calibration).
• Strong grasp of modern robot learning: reinforcement learning and/or imitation/diffusion policies, with working familiarity with VLAs and/or world models.
• Experience scaling data generation and collection for policy training (synthetic data, procedural generation, teleop/demonstration pipelines).
• Proficient in Python (and comfortable dropping into C++ where it counts); fluent with PyTorch or JAX and the modern ML tooling stack.
• A track record of getting robots actually to do the task in the real world: deformable or rigid/articulated manipulation on arms, bimanual setups, or humanoids.

Nice to Have (Strong Pluses)

• Using agentic / LLM-based tooling to automate sim and data work: reward generation (Eureka-style), automated scene/task generation, or agentic evaluation and curriculum design.
• Deformable object manipulation and the sim-to-real challenges that come with it (cloth, cable, soft bodies, contact-rich tasks).
• Experience with VLAs (e.g., OpenVLA, π0, RT-style policies) or world models for manipulation.
• Bimanual or whole-body / wheeled-humanoid manipulation.
• GPU-accelerated / massively parallel simulation and large-scale RL training.
• Real-time / on-robot deployment experience and familiarity with the controls and hardware stack, so your policies play nicely with the low-level controllers.
• Publications, open-source contributions, or competition results in robot learning or simulation.