In this role, you’ll work closely with engineering, product, and research teams to produce high-quality annotations that directly improve robotic system performance. Your work will help train models, refine data quality standards, and shape annotation guidelines and workflows.
We welcome candidates from a variety of backgrounds, including robotics, computer vision, video annotation, linguistics, STEM fields, quality assurance, research, and military experience.
Prior robotics experience is not required for consideration; however, candidates with 2–3 years of relevant robotics experience who also meet the preferred qualifications may be considered for the higher end of the compensation range.
The ideal candidate is detail-oriented, reliable, and motivated to contribute to real-world robotics systems. You have strong spatial reasoning, communicate observations clearly, and thrive in deadline-driven environments.
As a Robotics Data Annotator, you will:
- Review robot demonstration and teleoperation videos to segment tasks, subtasks, and manipulation events.
- Write clear, precise descriptions of robot actions and behaviors within each segment.
- Annotate perceptual data, including drawing bounding boxes, labeling object classes, and tracking objects across frames.
- Follow detailed annotation guidelines to ensure consistent, high-quality outputs.
- Identify edge cases, ambiguities, and failure modes, and escalate findings with actionable feedback.
- Perform quality assurance checks on your own and peers’ work to maintain data integrity.
- Execute annotation tasks across multiple robot platforms, environments, and use cases.
- Contribute to improving annotation tools, guidelines, and workflows.
Role specifics and target candidate profile:
- Ideal candidate profile Prior experience annotating robotics, autonomous vehicle, or computer vision data — particularly temporal segmentation, action labeling, bounding boxes, or object tracking. Candidates with this background are strongly preferred.
- Background in robotics, mechanical engineering, or a related field that provides intuition for how robots move and interact with objects.
- Experience with video annotation workflows (frame-level or clip-level labeling, keyframe interpolation, multi-object tracking).
- Familiarity with command-line interfaces, GitHub, or version-controlled environments.
- Daily tasks Review robot demonstration and teleoperation videos to identify and segment temporal boundaries of discrete tasks, subtasks, and manipulation events.
- Write clear, precise natural language descriptions of robot actions, behaviors, and task stages within each temporal segment.
- Annotate perceptual data including drawing bounding boxes around objects, labeling object classes, and tracking objects across video frames.
- Follow and interpret detailed annotation guidelines to ensure consistent, high-quality labels across large datasets.
- Identify edge cases, ambiguities, and failure modes in robot behavior and escalate them to engineering and development teams with clear, actionable feedback.
- Perform quality assurance checks on your own and peers’ annotations to maintain data integrity.
- Execute annotation projects across multiple robot platforms, environments, and task types as assigned.
- Participate in projects to improve annotation tools, guidelines, workflows, or operational processes.
Required skills
- Ability to perform focused, repetitive annotation tasks for extended periods with sustained attention to detail.
- Strong spatial reasoning and ability to interpret robot movement, object interactions, and manipulation sequences from video.
- Excellent written communication skills — you can describe physical actions and spatial relationships in clear, concise language.
- Proficiency using Windows, macOS, and/or Linux operating systems and office productivity tools.
- Ability to type at least 40 words per minute.
- Flexibility to work varying schedules is required and may include different shifts, extended hours, evenings, weekends, or holidays based on operational needs.
Pay: $33.00 - $40.00 per hour
Expected hours: 40.0 per week
Work Location: In person
