Jobs at UMA

Build and lead the end-to-end supply chain for scaling humanoid robots from prototypes to volume production. Responsibilities include supply chain strategy, BOM and DFM collaboration, supplier and manufacturing partner development, procurement, quality, logistics, import/export, ERP and KPI implementation, demand planning, inventory strategy, risk mitigation, and team leadership.

Manage the end-to-end development of a robotics platform from prototype through certified production. Coordinate hardware, AI/software, go-to-market, supply chain, and QHSE teams; own milestones, budgets, risks, critical paths, design reviews, build gates, compliance testing, technical files, status reporting, and escalation.

Build UMA’s recruiting function from the ground up, owning full-cycle hiring for AI, robotics, hardware, and software roles. Responsibilities include sourcing, managing applications and pipelines, setting hiring priorities, improving interview processes, preparing offers, using AI recruiting tools, and supporting employer branding through events and content. The role partners closely with founders and the People Lead and is based onsite in Paris.

Develop efficient architectures, training methods, scaling strategies, and hardware-aware implementations for robotic foundation models. Conduct controlled experiments, scaling-law studies, ablations, and performance benchmarks across pretraining, mid-training, and post-training. Optimize convergence, compute, memory, numerical stability, and kernel execution, then integrate validated methods into scalable training systems and assess transfer to real-robot capabilities.

Build and optimize distributed ML training infrastructure for humanoid robot foundation models across thousands of GPUs. Responsibilities include large-scale pretraining, RL and post-training systems, GPU and memory optimization, accelerated kernels, data movement, numerical debugging, deterministic execution, checkpointing, fault tolerance, observability, and reproducibility. The role partners closely with AI researchers to convert experimental code into reliable, scalable training systems.

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

Research data-centric methods that improve humanoid robot learning. Responsibilities include designing multimodal datasets, data curation and selection tools, training-stage data composition, controlled ablations, scaling studies, and real-robot evaluation. The role connects research findings to data collection strategy and reproducible training workflows, collaborating with AI, robotics, infrastructure, and data engineering teams.

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

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.

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.

Owns finance, legal, and administrative operations for a deep-tech startup. Responsibilities include fundraising support, investor reporting, cash forecasting, budgeting, grant and tax incentive applications, cap table and contract administration, supplier payments, banking, quarterly close, and finance automation. The role partners closely with the CFO, founders, external accountants, counsel, and DevOps while building scalable processes in a fast-growing 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.