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About the Role
World Labs is seeking a Robot Learning Engineer/Scientist to develop and advance state-of-the-art methods for robot policy development. This role focuses on training end-to-end policies with an emphasis on sim-to-real transfer, robust performance, and scalable training and inference pipelines. It's a hands-on, research-driven position at the intersection of robotics and machine learning, requiring collaboration with research scientists, ML engineers, and system teams to translate robotic policies into production-ready systems.
Responsibilities
- Design and implement modern robot learning systems, including imitation learning and reinforcement learning for manipulation.
- Research, prototype, and productionize robotic policies with a focus on speed, precision, and scalability.
- Develop and improve training pipelines for sim-to-real transfer, including domain randomization, system identification, and real-sim alignment.
- Collaborate with simulation and infrastructure teams to minimize the sim-to-real gap and ensure learning methods integrate cleanly with real-robot deployment stacks.
- Build end-to-end training and evaluation workflows for robot policies, from large-scale data generation to scaling up training and evaluation.
- Optimize policy performance across the stack, including training speed, inference latency, and data generation efficiency to support large-scale production constraints.
- Diagnose failure modes in simulation and real-world rollouts, and design principled solutions to improve robustness, efficiency, and generalization.
- Contribute to technical direction by proposing new research ideas, mentoring teammates, and helping set best practices for robot learning across the organization.
Requirements
- 6+ years of experience working on manipulation, locomotion, robot policy training, or related areas.
- Strong foundation in robotics, neural network designs, and sim-real transfer.
- Deep experience with robot policy designs (e.g., VLA, WAM, diffusion).
- Proficiency in Python and/or C++, with hands-on experience building research or production robotic systems.
- Experience with deep learning frameworks (e.g., PyTorch) and low-level robotic controllers.
- Proven ability to work in ambiguous, fast-moving environments and drive projects from concept through deployment.
- A strong sense of ownership and engineering rigor, with a focus on correctness, stability, and measurable improvements.
- Enjoy collaborating with a small, high-caliber team and raising the technical bar through thoughtful design, experimentation, and code quality.
Skills
- Robot Learning
- Imitation Learning
- Reinforcement Learning
- Manipulation
- Sim-to-Real Transfer
- Domain Randomization
- System Identification
- Real-Sim Alignment
- Robotics
- Neural Networks
- Python
- C++
- PyTorch
- Robotic Controllers
Location
- San Francisco Bay Area
Work Type
- Full-time
Experience Level
- 6+ years
Salary/Compensations
- $250,000 - $350,000 base salary
Benefits
- Equity awards
About the Company
- World Labs is a frontier AI research and product company advancing spatial intelligence, the next frontier beyond large language models.
- Co-founded by Dr. Fei-Fei Li, Justin Johnson and Ben Mildenhall, the company is pioneering world models that perceive, generate, reason, and interact with virtual and physical worlds.
- The company’s flagship product, Marble, transforms text, images, and video into fully navigable 3D worlds, unlocking applications across gaming, film, architecture, robotics, and immersive digital experiences.
- Backed by leading investors and with over $1B raised, World Labs is assembling a world-class team at the intersection of AI research and real-world deployment.
Equal Opportunity
- World Labs is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected under applicable law. We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.