Data Annotation Specialist, Physical AI

Genesis AI is building a universal robotics foundation model — the "brain" that lets robots see, reason and handle objects in the real world. Our models learn from large volumes of robot, simulation and human-demonstration data, and the quality of that data directly shapes how well our robots perform. We're scaling our labeling team. You'll review and annotate robot and video data that trains and evaluates our models, working side by side with the researchers who use it. Your judgment on hard cases will shape our guidelines, our tools and, ultimately, how our robots behave.

Responsibilities

Annotate robot episodes, videos, images, and sensor data using internal labeling tools; Label objects, actions, task steps, and successes or failures in robot demonstrations; Write clear, consistent natural-language descriptions of robot and human behavior; Flag ambiguous or edge cases and help define how they should be handled; Perform quality assurance checks on teammates' annotations and join regular calibration sessions; Meet quality and throughput targets while tracking your own accuracy over time; Report tool bugs, workflow friction, and guideline gaps to the engineering team

Requirements

2-5+ years of data annotation or labeling experience in a detail-heavy, deadline-driven environment; A strong sense of ownership: you see work through to the finish line; Drive and urgency: you're hungry to learn, move fast, and keep going on tight deadlines; A track record of high-accuracy work at volume, with the ability to hold quality while speed increases; Sharp attention to detail and the judgment to spot when instructions fall short; Excellent written communication, and the confidence to raise problems early; Comfort with ambiguity and shifting priorities in a fast-moving startup; Curiosity, coach-ability, and consistency

Nice to have

Prior data annotation, quality assurance, or issue triage experience; Experience with video, 3D, point-cloud, text, or robotics data; Interest in robotics, machine learning, or how physical tasks are performed