<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>Research Scientist/ Engineer - Diffusion Policies for Robotic Learning</strong></p><p>Zurich (Relocation + Sponsorship Available)</p><p><br/></p><p>Our client is a well-funded world model lab building generative, interactive environments that other companies build on top of. They're focused on making world models physically grounded and reliable in specific domains, where passive video data isn't enough.</p><p><br/></p><p>This role uses robotics-style policy learning to generate the action-conditioned data that trains those world models. The work is about manufacturing training signal through control, not deploying robots in production. (This is a research role feeding world models, not a robotics product role.)</p><p><br/></p><p><strong>What you'll work on</strong></p><ul><li>Training diffusion and flow-matching policies to generate action-conditioned interaction data for world models</li><li>Building systems that collect data through control to cover dynamics that scraped video can't reach</li><li>Designing training objectives and evaluation metrics for data quality and world model improvement</li><li>Running experiments end-to-end (data collection → training → evaluation) on real manipulation hardware</li></ul><p><br/></p><p><strong>What we're looking for</strong></p><ul><li>Strong robot learning background: imitation learning, RL, or both, on real manipulation</li><li>Hands-on experience with diffusion policies, flow matching, or related generative approaches to control</li><li>Experience with world models, video generation, or action-conditioned prediction</li><li>Strong ML/RL fundamentals: policy optimisation, reward design, evaluation</li><li>Ability to run experiments end-to-end and iterate quickly</li></ul><p><br/></p><p><br/></p><p><strong>Bonus</strong></p><ul><li>Publications or open-source contributions in robot learning, RL, or generative modelling</li><li>Experience with large-scale or distributed model training</li><li>Dexterous or bimanual manipulation experience</li></ul><p><br/></p><p>Our client is an equal opportunity employer and welcomes applicants from all backgrounds. All qualified candidates will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.</p> </div>