Senior Robotics Data Engineer, Frontier AI & Robotics (FAR)

Amazon; San Francisco (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

Amazon is seeking to advance frontier robotics through its Frontier AI & Robotics (FAR) program. The role focuses on owning and optimizing data collection for early-stage robotics research, partnering with researchers, engineers, and operators to ensure high-quality data while maintaining safe, scalable collection practices.

You will guide collectors, calibrate workflows, and implement reusable tooling to minimize failures and improve turnaround times.

Qualifications

• Experience designing reliable data collection workflows for research tasks.
• Ability to translate research objectives into practical collection processes.
• Familiarity with robotics sensors, calibration, and data integrity concepts.

Responsibilities

• Own end-to-end collection iterations from requirements to delivery of training-ready data.
• Coordinate dependencies and resolve blockers to meet quality and turnaround goals.
• Develop and refine SOPs for collection workflows and ensure consistent adoption.
• Act as the technical expert for collection workflows, equipment, and tools.
• Provide coaching to data collectors and improve troubleshooting skills.
• Assess risks to data collection and coordinate safe recovery actions.
• Track metrics like usable data yield and iteration timing to drive improvements.
• Lead improvements to reduce collection failures and drive adoption across stations and teams.
• Communicate priorities and risks clearly to researchers, engineering, operations, and partners.

Skills

Data collection workflow design

Robotics hardware knowledge

Quality data assurance

Stakeholder coordination

Job description

Are you interested in solving the most critical operations problem behind cutting-edge robotics development? Interested in fast dynamic environment? Amazon's Frontier AI & Robotics (FAR) team is building advanced robotic systems through frontier foundation models, and high-quality data collection is a core input to that research.

Key job responsibilities

• Own the technical execution and continuous improvement of early-stage data collection for advanced robotics. Partner with researchers to translate research objectives into reliable collection workflows, improve data quality, and shorten policy training iteration cycles. Work independently within agreed priorities, providing hands‑on technical guidance to data collectors and coordinating execution across research, engineering, and operations.

In this role, you will

• Own end-to-end collection iterations for assigned research tasks, from requirements and station readiness through pilot collection, quality review, and delivery of training-ready data. Coordinate dependencies and resolve blockers to meet agreed quality and turnaround goals.
• Partner with researchers to develop and refine SOPs, translating task objectives, failure modes, and evaluation feedback into clear collection instructions and acceptance criteria. Maintain version control and ensure collectors adopt changes consistently.
• Serve as the technical subject matter expert for assigned collection workflows, equipment, and tools. Independently diagnose complex issues involving robotics hardware, sensors, calibration, collection software, and data integrity; engage engineering partners when resolution requires deeper expertise.
• Provide critical technical direction and coaching to data collectors. Demonstrate correct execution, calibrate collectors against quality standards, and develop their troubleshooting skills and judgment.
• Assess operational and technical risks, including equipment readiness, unsafe conditions, data loss, and quality degradation. Determine when to continue, pause collection, or escape, and coordinate recovery within established safety and technical boundaries.
• Own collection performance metrics, including usable‑data yield, rework, station availability, and iteration turnaround time. Use dashboards and root‑cause analysis to identify recurring issues and verify that corrective actions improve results.
• Lead operational improvement projects that reduce collection failures and iteration delays. Develop and implement reusable workflows, troubleshooting guides, readiness checks, and lightweight tooling, and drive adoption across relevant stations, shifts, and partner teams.
• Coordinate technical and nontechnical stakeholders, including researchers, engineering, operations, and annotation partners. Communicate priorities, trade‑offs, risks, and decisions clearly, and elevate issues that threaten delivery commitments.