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Compute Infrastructure Lead

UMA
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Paris, FranceFull-timePosted 1mo ago

About the role

Lead the compute backbone for UMA's training, evaluation, and data-processing workloads. Build and operate a multi-provider, production-scale compute fabric with scheduling, virtualization, observability, and cost control to move models from research to production.

Responsibilities

  • Own our compute platform end to end — from provisioning GPU capacity across cloud providers to keeping training, eval, and processing jobs running at high utilization, with reliability, cost, and researcher velocity as first-class goals
  • Build a multi-provider management layer to place, burst, and fail over workloads across GPU clouds, hyperscalers, and HPC without rewriting jobs
  • Design and operate the cloud scheduler — quotas, priority, preemption, topology-aware placement, and dynamic checkpointing so jobs survive node failure, preemption, and provider switches
  • Stand up a distributed compute framework for training and evaluation on heterogeneous hardware (e.g. Ray / similar), including the real-time / online-learning path
  • Orchestrate data-processing workloads at scale — CPU and cheaper GPUs, batch and streaming
  • Deliver virtualized GPU/CPU dev sessions (VMs on the cluster) for interactive engineering on the same hardware/software used in training
  • Build observability that works from any provider — system metrics (Prometheus, Grafana), job traces/logs, and model metrics (e.g. MLflow)
  • Own capacity, cost, and provider relationships as a technical lead: forecast demand, select hardware/contracts, negotiate pricing and terms
  • Help set production-grade practices (testing, reliability, fast iteration) as we move from R&D to partner POCs and grow the compute infra team if desired

Qualifications

  • 8+ years in ML/compute infrastructure, HPC, or large-scale GPU platform engineering (senior/lead/staff)
  • Proven track record building and operating infrastructure for large-scale AI model training (multi-node GPU clusters, distributed training with PyTorch/NCCL or equivalent)
  • Deep hands-on experience with GPU clouds and cluster operations (provisioning, Linux, high-performance networking such as InfiniBand/ROCE, storage for training, GPU/node failure modes)
  • Built or owned schedulers and distributed frameworks (Slurm, Kubernetes, Ray, SkyPilot, or similar) with checkpointing, elasticity, and preemption
  • Strong focus on observability, reliability, and cost as engineering concerns
  • Strong Python and systems engineering skills, with ability to build researchers-friendly tooling
  • Experience working with GPU providers on capacity and commercial terms, able to read contracts and debug clusters at 2am
  • Ability to reason about end-to-end systems — performance, scalability, reliability, cost
  • Thrives in a hands-on, fast-paced startup environment, capable of building from a small cluster toward production fleet
  • Bonus: online/continuous RL, real-time training loops, always-on learning systems
  • Bonus: multi-cloud/multi-provider fabrics, Ray/Anyscale, HPC centers, VM-based GPU workstations or scalable clusters
  • Bonus: robotics/autonomous vehicles/embodied AI training, or related large-scale training stacks
  • Bonus: public projects, open-source contributions, maintained tools, or technical writing

Skills

Required:ML/compute infrastructure leadershipDistributed scheduling and frameworksGPU cloud operationsObservability and reliability
Last updated Sep 17, 2026. Interested? Apply on the company site.

About UMA

UMA - Building intelligent robots that enhance quality of life for everyone

At UMA, we develop AI and robotics dedicated to shaping a new economic and societal era. We design general-purpose mobile and humanoid robots with human-level dexterity and a deep understanding of the physical world.

AIBuilding the futureDeep LearningGeneral-purpose robotsLeRobotRoboticsheadline-ai-europe-100-2026intelligent-hardware
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