Data Operations Supervisor- 2nd shift

Boston Dynamics•Savannah, GA

9d•$102,000 - $128,000•Onsite

About The Position

Boston Dynamics is a world leader in mobile robots, tackling some of the toughest robotics challenges. We combine the principles of dynamic control and balance with sophisticated mechanical designs, cutting-edge electronics, and next-generation software for high-performance robots equipped with perception, navigation, and intelligence. The RMAC team plays a critical role in advancing this mission by delivering high-impact capabilities that enable scalable, reliable, and cutting-edge applications across our platforms. The Data Operations team on the Atlas Robot is responsible for collecting new data to train Atlas’s brain. Individuals on the team will teleoperate robots, wear sensors while performing tasks, and other techniques. We are seeking a Second Shift Supervisor, who will be responsible for the execution of a team of Data Operations associates from 2:30pm - 11pm weekdays. Key KPIs for this role include hours of data collected/shift, yield and quality rate of data collected, and ensuring strong safety and operational excellence.

Requirements

Prior experience coaching and optionally managing individuals in a professional environment such as retail, construction, manufacturing, or logistics.
Data analysis skills, such as reviewing KPIs, building or using dashboards, and constructing spreadsheets.
Strong written and verbal communication skills. Email, chat, Jira, slide decks, and documents will all be part of the workflow.
Demonstrated history of operational excellence and continuous improvement.

Nice To Haves

Prior exposure to robotics, VR, and/or networking and IT technology.
Experience with Google Workspace, Jira, and other tools

Responsibilities

Oversee contractors and internal employees responsible for ML annotation tasks, robot teleoperation, and building data assets.
Establish clear documentation for onboarding and training teams involved in data-related processes through structured learning programs and on-the-floor coaching.
Develop and track KPIs for data quality, efficiency, and failure causes.
Interface directly with ML researchers and engineers to understand data requirements
Proactively identify bottlenecks and inefficiencies in the data pipeline, and work with Engineering and IT to develop improved solutions.
Track and manage the budget for the overall team.
Plan shift assignments based on data demand and personnel and equipment availability.
Ensure adherence to safety protocols.
Build and maintain physical space in cooperation with facilities and IT.
Analyze and report shift performance to other stakeholders.
Develop and deploy new SOPs as needed.
Trial new software, hardware, or processes.