Job description

OUR MISSION Expand human ambition in the physical world. Critical infrastructure is constrained by labor shortages, hazardous working conditions, and operational complexity. Watney builds and deploys autonomous robotic systems that increase the speed and capacity of buildout, starting with data centers. ABOUT THE ROLE At Watney, a tools engineer builds the tools that turn what our robots record in the field into data our models learn from. Engineers use the same tools to match a change in a robot’s telemetry to the moment an error happened. What a robot should learn from a day of real work is still an open question: which moments are worth training on, and which labels hold up under review. Some of our best labels come from records captured on site while the robots work. Data quality is model quality here. You will own the tools that decide what goes into a training set, and what those tools reveal about the data shapes where our R&D goes next. WHAT YOU’LL DO - Own the viewer engineers use to play back a robot’s data and line up telemetry with errors. - Own dataset curation: the dataset API and the tools engineers use to find and group episodes and build training sets. - Build annotation tooling with the rest of the software team, and weigh what we build against what we buy. - Build the quality review process for annotated data. - Work with the ML team who train on this data, the engineers who debug the fleet with it, and the annotators who label it. YOU MAY BE A GOOD FIT IF YOU: - Have built an internal tool that became the 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 people use to annotate data at volume. We’re committed to building a diverse, inclusive team. At Watney Robotics, we welcome people of all backgrounds and identities, and we make hiring decisions based on skills, experience, and potential. If you’re passionate about robotics but don’t meet every requirement, we still encourage you to apply! CURIOUS TO LEARN MORE? Follow us here on X x.com and LinkedIn linkedin.com

Requirements

Must-haves

Data quality is model quality here. You will own the tools that decide what goes into a training set, and what those tools reveal about the data shapes where our R&D goes next.
Own the viewer engineers use to play back a robot’s data and line up telemetry with errors.
Own dataset curation: the dataset API and the tools engineers use to find and group episodes and build training sets.
Build annotation tooling with the rest of the software team, and weigh what we build against what we buy.
Build the quality review process for annotated data.
Work with the ML team who train on this data, the engineers who debug the fleet with it, and the annotators who label it.
YOU MAY BE A GOOD FIT IF YOU:
Have built an internal tool that became the 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 people use to annotate data at volume.
CURIOUS TO LEARN MORE?

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

apirestml

Platform: ashby
Effort: quick