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This open candidate position invites individuals passionate about Robotics and AI to propose innovative ideas and solutions in a fast-paced startup environment.

Lead research on world models, planning, and reasoning for humanoid robots. Design and run experiments in simulation and on hardware, own data processing, model training, evaluation, and integration with control stacks, and document and mentor engineers to ship real-world capabilities.

Own and scale UMA’s compute infrastructure for large-scale AI training, evaluation, and data-processing workloads. Responsibilities include multi-provider GPU capacity, cloud scheduling, distributed training frameworks, checkpointing, preemption, virtualized development environments, observability, reliability, cost optimization, and provider relationships. The role requires hands-on operation of production-grade GPU clusters and may grow into leadership of the compute infrastructure team.

Own the whole-body controller and state estimation for a humanoid robot on real hardware. Responsibilities include designing locomotion and loco-manipulation controllers, floating-base estimation, contact detection, sensor fusion, real-time optimal control, actuator characterization, simulation-to-hardware deployment, model-based and learned policy integration, and diagnostic tooling.

Develop simulation environments for humanoid locomotion, implementing and testing control strategies, and enhancing motion through reinforcement learning and optimization.

Develop and implement learning-based control systems for humanoid robots, using reinforcement learning and other techniques, while optimizing performance and conducting experiments.

Implement and reproduce state-of-the-art world-model baselines, build training/data/evaluation pipelines, run latent-space planning and MPC experiments in simulation and on humanoid robots, and scale and debug distributed GPU training to keep experiments reproducible and logged.