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About Us
Our robots generate massive multi-modal data streams, from video, audio, proprioception, to control trajectories. To learn from this at scale, we build the simulation and data infrastructure that turns real-world and virtual experiences into structured data for embodied agents.
This role sits at the core of that system, creating the environments and data that power large-scale robot learning.
Role Overview
You will architect and maintain the simulations & data platform powering our robot learning stack, ensuring high-fidelity data capture, scalable synthetic data generation, and seamless real2sim/sim2real integration.
Responsibilities
- Develop and maintain simulation environments for embodied learning and reinforcement learning: using tools such as Isaac Sim, Mujoco/MJX
- Generate synthetic data at scale for vision, audio, and control tasks: integrate with model training pipelines (e.g., PyTorch, JAX, Ray, or RLlib)
- Design multimodal data systems: structuring streams from sensors, cameras, IMUs, and actuators into training-ready datasets
- Implement real2sim/sim2real adaptation techniques: domain randomization, latent-space alignment
- Collaborate on policy learning loops: connecting simulation rollouts to real-world deployment for continuous improvement
- Contribute to engine and infrastructure tooling: from low-level C++ optimization to high-level Python interfaces
Preferred Qualifications
- C++ and/or Python proficiency
- Experience with physics engines or robotics simulation software
- Deep interest or familiarity with Robotics simulators: Isaac Sim, Coppelia, Genesis, Bullet, or RaiSim, etc.
- Deep interest or familiarity with physics engines: MuJuCo/MJX, PhysX, Pybullet, or Havok, etc.
- Deep interest or familiarity with tools: ROS, Moveit, etc
- Bonus: Strong background in physics, graphics, or math
- Bonus: Practical experience implementing Vision-Language Action Models in robotic systems
Bonus Skills
- Built or contributed to robotic simulations or data systems.
- Experience with foundation model data curation (tokenization, sharding, filtering).
- Strong interest in enabling embodied AI through scalable data infrastructure.
Menlo Research Singapore Office
What you need to know about the Singapore Tech Scene
The digital revolution has driven a constant demand for tech professionals across industries like software development, data analytics and cybersecurity. In Singapore, one of the largest cities in Southeast Asia, the demand for tech talent is so high that the government continues to invest millions into programs designed to develop a talent pipeline directly from universities while also scaling efforts in pre-employment training and mid-career upskilling to expand and elevate its workforce.
