Research Engineer / Scientist (robot Learning)

World Labs World Labs · AI Frontier · San Francisco, CA · Research

Research Engineer/Scientist focused on developing and advancing state-of-the-art methods for robot learning policies, with an emphasis on sim-to-real transfer, scalable training, and inference pipelines. The role involves designing, implementing, and productionizing robot learning systems, including imitation and reinforcement learning for manipulation, and optimizing policy performance across the stack.

What you'd actually do

  1. Design and implement modern robot learning systems, including imitation learning, reinforcement learning for manipulation.
  2. Research, prototype, and productionize robotic policies with a focus on speed, precision and scalability.
  3. Develop and improve training pipelines for sim-to-real transfer, including domain randomization, system identification, real-sim alignment.
  4. Collaborate with simulation and infrastructure teams to minimize sim-to-real gap and ensure learning methods integrate cleanly with real-robot deployment stacks.
  5. Build end-to-end training and evaluation workflows for robot policies, from large-scale data generation to scale up training and evaluation.

Skills

Required

  • Python
  • C++
  • PyTorch
  • Robotics
  • Neural Network Designs
  • Sim-real Transfer
  • Robot Policy Designs
  • Deep Learning Frameworks
  • Low-level Robotic Controller

Nice to have

  • Imitation Learning
  • Reinforcement Learning
  • Manipulation
  • Locomotion
  • Domain Randomization
  • System Identification
  • Real-sim Alignment

What the JD emphasized

  • 6+ years of experience working on manipulation, locomotion, robot policy training, or related areas.
  • Strong foundation in robotics, neural network designs, sim-real transfer.
  • Deep experience with robot policy designs (e.g., VLA, WAM, diffusion).
  • Proven ability to work in ambiguous, fast-moving environments and drive projects from concept through deployment.
  • A strong sense of ownership and engineering rigor: you care deeply about correctness, stability, and measurable improvements.

Other signals

  • robot learning
  • sim-to-real transfer
  • policy training
  • inference optimization