Data & ML Infrastructure Lead

Your Mission

As Data & ML Infrastructure Lead, you will own and scale the data backbone of UMA. This includes the systems that record, store, version, serve, and visualize the data our robots produce, and the infrastructure that turns that data into trained policies. Data is a core challenge in robotics AI, and we’re looking for someone who has built data infrastructure at scale to take ownership.

We already have a working data and training stack and a strong team behind it, so you won’t be starting from zero but you’ll have the mandate to shape the best possible architecture. You’ll take ownership of the data platform end to end: recording, storage, versioning, high‑throughput loading for training, and tooling to explore and debug our data at scale, and quickly grow into leading the Data & ML Infrastructure team as it scales. The goal is a tight, scalable loop—a data engine where our fleet continuously produces data, we learn from it in near real time, and we ship improvements back fast—much of which doesn’t exist off the shelf. The architectural decisions you make now will determine our success. Compute and training infrastructure (GPU clusters, distributed training, scheduling) is a secondary but growing part of the role; the data platform remains the core today.

Key Responsibilities

• Lead the design of our data platform end to end—from on‑robot capture of multimodal data (video, depth, proprioception, actions, sensors) to training‑ready datasets—built for reliability, scale, and reproducibility.
• Build versioned, orchestrated pipelines (e.g. Airflow) for post-processing and dataset statistics, so every run is fully reproducible.
• Make training data loading fast with efficient video decoding, prefetching, and a training‑optimized format that keeps GPUs fed.
• Produce and manage datasets from reinforcement learning and continuous‑learning loops, closing the loop between deployment and training.
• Design and build visualization, exploration, and metadata tooling to inspect, curate, and debug our data at scale—a central part of our data‑centric strategy.
• Design storage and transfer systems with formats suited to both exploration and high‑throughput training, scaling cost‑effectively as data and fleet grow.
• Grow our compute and training infrastructure (GPU clusters, distributed training, scheduling) as need arises, and help establish data standards and production‑grade practices as the team expands.

What You Bring To The Table

• 8+ years in data infrastructure, ML infrastructure, or large‑scale data‑platform engineering, at a senior, lead, or staff level.
• Proven track record building data‑intensive infrastructure in production and at scale—distributed data processing (e.g. Spark/Ray), workflow orchestration, cloud—with a data flywheel powering a continuously improving ML system.
• Deep experience with large‑scale multimodal and time‑series data (video, sensor streams, high‑frequency signals) and the storage systems behind it—object storage (e.g. S3) for media, databases for structured data.
• Hands‑on experience optimizing training data pipelines—loading throughput, video decoding, prefetching, keeping GPUs fed.
• Treat versioning, lineage, observability and reproducibility as core engineering concerns.
• Strong Python skills, with solid experience in a high‑performance compiled language (C++, Rust) and a taste for building tooling that is reliable, maintainable, and pleasant to use.
• Good grasp of compute/training infrastructure (GPU clusters, distributed training, Slurm, cloud) or the clear ability to grow into it.
• Ability to reason about systems end‑to‑end—performance, scalability, reliability, cost—and make and defend the right trade‑offs.
• Thrives in a hands‑on, fast‑paced startup, building from scratch as the company grows: autonomous, rigorous, execution‑driven, easy to work with, and broadly curious about AI, robotics, and systems.
• Bonus: robotics, autonomous vehicles, or other embodied/physical‑AI data (adjacent large‑scale multimodal—AV, video, geospatial/sensor—counts strongly), reinforcement learning/continuous‑learning loops, or fleet‑scale data collection.
• Bonus: public projects, open‑source contributions, maintained tools, or technical writing.
• We value exceptional builders over perfect resumes. If you have a world‑class data‑infrastructure track record and the drive to build the backbone that lets a robotics company scale, we strongly encourage you to apply—even if you don’t tick every box.