Robotics Data Engineer - Workflow & Quality Lead

Amazon Inc.; San Francisco (CA); In loco; USD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

Amazon Frontier AI & Robotics is seeking a hands-on data collection lead to drive end-to-end workflows for advanced robotics research. You will translate research objectives into reliable collection processes, ensure high data quality, and guide a team across researchers, engineering, and operations.

You will work independently within priorities, providing technical direction and coaching to data collectors, and help shorten policy training iteration cycles through robust data collection

Mansioni

• Own end-to-end collection iterations for assigned research tasks, from requirements and station readiness through pilot data collection, quality review, and delivery of training-ready data.
• Coordinate dependencies and resolve blockers to meet quality and turnaround goals.
• Develop and refine SOPs, translating task objectives and evaluation feedback into clear collection instructions and acceptance criteria.
• Act as technical SME for data collection workflows, equipment, and tools; diagnose issues and engage engineering when needed.
• Provide daily technical direction and coaching to data collectors, calibrating against quality standards.
• Assess risks including equipment readiness, unsafe conditions, data loss, and quality degradation, and coordinate recovery within safety boundaries.
• Coordinate stakeholders across researchers, engineering, operations, and annotation partners; communicate priorities, risks, and decisions clearly.

Descrizione del lavoro

Amazon Frontier AI & Robotics is seeking a hands-on data collection lead to drive end-to-end workflows for advanced robotics research. You will translate research objectives into reliable collection processes, ensure high data quality, and guide a team across researchers, engineering, and operations.

You will work independently within priorities, providing technical direction and coaching to data collectors, and help shorten policy training iteration cycles through robust data collection