Job Opportunities API

About World Labs: We build foundational world models that can perceive, generate, reason, and interact with the virtual and physical worlds — unlocking AI's full potential through spatial intelligence by transforming seeing into doing, perceiving into reasoning, and imagining into creating. We believe spatial intelligence will unlock new ways of developing autonomous agents in the real world. We bring together a world-class team, united by a shared curiosity, passion, and deep backgrounds in technology — from AI research to systems engineering to product design — creating a tight feedback loop between our cutting-edge research and products that empower our users. 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 bright