Data Operations Lead

UltraBrooklyn, OH

About The Position

You will own the programs that turn research priorities into usable robotics datasets, working with ML researchers, robotics engineers, data engineers, and operations teams to determine what data we need, translate those requirements into collection and annotation programs, and ensure the resulting datasets are delivered with the required quality, coverage, and timing. This is a technically demanding program role. You will not directly build every tool or run every collection shift, but you will understand the complete data lifecycle and drive coordinated execution across the teams responsible for it.

Requirements

  • Significant experience in technical program management, ML operations, robotics, autonomy, data platforms, or a related technical field.
  • Managed complex programs spanning research, software, hardware, and operations.
  • Understand the fundamentals of ML training data, evaluation, dataset quality, and experimentation.
  • Communicate clearly in technical specifications, program reviews, and executive updates.
  • Thrive in an environment where requirements and priorities change quickly.
  • Comfortable getting your hands dirty, whether that means writing some scrappy code or hopping on a flight to get things done

Nice To Haves

  • Experience with robotics, autonomous vehicles, embodied AI, or multimodal ML.
  • Experience with video, sensor, trajectory, or teleoperation datasets.
  • Familiarity with SQL, Python, or direct dataset analysis.
  • Experience designing annotation ontologies or evaluation datasets.
  • Experience coordinating external data vendors or collection partners.

Responsibilities

  • Operationalize the execution of the collection, annotation, validation, and management of our data
  • Translate model-training and evaluation priorities into concrete data requirements, task definitions, collection plans, and acceptance criteria.
  • Own the roadmap and delivery plan for major robotics data programs.
  • Continually improve our process around collecting data and running evals
  • Build internal tools and software to streamline and make our process more efficient
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