Research Engineer - Embodied World Models

At UMA, we’re pushing the frontier of physical AI by teaching robots to understand, plan, and reason in the physical world. A critical part of this effort is building world models that can predict the outcomes of actions before they are executed on a robot.

As a Research Engineer on the World Models team, you’ll be the force that quickly turns bold research ideas into working systems. You’ll build the pipelines, baselines and experiments that let our scientists go from hypothesis to results and humanoid robots that leave the lab for the real world.

Key Responsibilities:

• Implement and reproduce state-of-the-art world-model baselines, run ablations and run them on real robots.
• Build and own the training, data and evaluation pipelines for self-supervised learning on video and action data.
• Bring latent-space planning and model-predictive control experiments to life on real hardware and in simulation.
• Profile, debug and scale distributed training runs, keep every experiment clean, logged and reproducible.

• Strong software engineering and machine learning fundamentals, fluent in Python and PyTorch, comfortable with distributed/GPU training.
• A solid grasp of self-supervised representation learning; exposure to video models, RL, planning or robotics is a real plus.
• MSc or PhD in ML, CS, robotics or a related field, what matters most is what you’ve built and shipped.
• Experience with world models (e.g. JEPA) is highly valued.
• Bring curiosity, rigor, and a bias for getting things working.
• Bonus: You’ve worked in early-stage startups or the core teams of big companies building humanoids or highly-dexterous robots.
• UMA is an inclusive workplace that values exceptional builders over perfect pedigrees. Whatever your background, identity, or journey, if you don't meet every criterion but believe you can have an outsized impact here, we strongly encourage you to apply.