Job description

About World Labs 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. Role Overview We’re looking for strong Robot Learning Engineer/Scientist to develop and advance state-of-the-art methods for developing robot policies. This role is focused on training end-to-end policies with an emphasis on sim-to-real transfer, robust performance, scalable training and inference pipelines. This is a hands-on, research-driven role for someone working at the intersection of robotics and machine learning. You’ll collaborate closely with research scientists, ML engineers and system teams to translate robotic policy and its stack into production-ready systems. What You Will Do Design and implement modern robot learning systems, including imitation learning, 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, real-sim alignment. Collaborate with simulation and infrastructure teams to minimize 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 scale up training and evaluation. Optimize policy performance across the stack, including training speed, inference latency, data generation efficiency to support large scale production constraints. Diagnose failure modes in simulation and real-world rollouts, 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. Key Qualifications 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). 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 controller. 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. Enjoy collaborating with a small, high-caliber team and raising the technical bar through thoughtful design, experimentation, and code quality. Who You Are Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible. Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger. Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people. Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence. We're hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you're ready to work on technology that will reshape how machines perceive and interact with the world - then World Labs is your launchpad. Join us, and let's make history together. Equal Opportunity & Pay Transparency Equal Employment 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. California Pay Transparency In accordance with California law, we disclose the following: Pay Range $250,000 - $350,000 base salary (good-faith estimate for San Francisco Bay Area upon hire; actual offer based on experience, skills, and qualifications) Total Compensation Base salary plus equity awards Salary History We do not request or consider prior compensation in making offers Compliance: Cal. Lab. Code §432.3 (pay scale disclosure & salary history ban); Cal. Lab. Code §1197.5 (Equal Pay Act); Cal. Gov. Code §12940 (FEHA); 42 U.S.C. §2000e (Title VII); 29 U.S.C. §621 (ADEA); 42 U.S.C. §12101 (ADA)

Requirements

Must-haves

Design and implement modern robot learning systems, including imitation learning, 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, real-sim alignment.
Collaborate with simulation and infrastructure teams to minimize 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 scale up training and evaluation.
Optimize policy performance across the stack, including training speed, inference latency, data generation efficiency to support large scale production constraints.
Diagnose failure modes in simulation and real-world rollouts, 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.
Key Qualifications
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).
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 controller.
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.
Enjoy collaborating with a small, high-caliber team and raising the technical bar through thoughtful design, experimentation, and code quality.
Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible.
Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger.
Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people.
Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence.
Join us, and let's make history together.
Equal Opportunity & Pay Transparency

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpython

Platform: greenhouse
Effort: quick