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Job Description

Build the internal tools that turn robot field recordings into high-quality training data.

Responsibilities

  • Own the viewer experience used to play back robot data and align telemetry with observed errors
  • Own dataset curation, including the dataset API and tooling for finding, grouping episodes, and constructing training sets
  • Build annotation tooling alongside the software team, including evaluating tradeoffs between building tools versus buying existing solutions
  • Develop and maintain the quality review process for annotated data
  • Collaborate with the ML team that trains on the data, fleet debugging engineers who use it for diagnosis, and annotators who label it

Requirements

  • Have built an internal tool that became the standard way a team works
  • Have built visualization or analysis tools for large volumes of sensor and video data
  • Have built dataset tooling or a data pipeline that ML engineers train from
  • Have designed a labeling or review workflow that enables annotation at volume

About the role

  • Tools engineering focuses on converting what robots record in the field into data models can learn from
  • Use cases include matching changes in robot telemetry to the moment an error occurred
  • Own decisions about what belongs in training sets and what those tools reveal about the data patterns that guide next R&D steps
  • Data quality is treated as model quality

Mission

  • Expand human ambition in the physical world.

Location

  • San Francisco, CA (onsite)

Company values

  • Watney Robotics welcome people of all backgrounds and identities
  • Hiring decisions are based on skills, experience, and potential
  • Encourages applicants who are passionate about robotics even if they do not meet every requirement

Follow Watney on X and LinkedIn to learn more.

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