Vision-Language-Action (VLA) Annotator

• Review and annotate video footage, sensor telemetry, and camera feeds from autonomous vehicle test drives and robotics platforms.
• Assess vehicle and robotic behavior in 3D space using 2D camera inputs, including approach angles, following distances, trail alignment, and controlled stop quality.
• Use time-series telemetry data — including speed, throttle, steering, and braking charts — to make precise trim and segmentation decisions on data clips.
• Apply annotation guidelines consistently while exercising independent judgment on ambiguous or edge-case scenarios.
• Identify and flag unsafe, incomplete, or anomalous driving behaviors (e.g., rolling stops, improper following distance, out-of-distribution maneuvers).
• Maintain high throughput and accuracy standards; participate in regular quality audits and calibration sessions.
• Work within annotation platforms (e.g., Encord, CVAT, Label Studio, or similar) to complete labeling tasks efficiently.
• Document and communicate recurring issues or ambiguities in the data to improve pipeline quality.

• Education: Bachelor's degree with a STEM background preferred (Engineering, Computer Science, Physics, Mathematics, GIS, or related field).
• Spatial & Mechanical Reasoning: Demonstrated ability to interpret vehicle or robotic behavior in 3D space from 2D camera feeds. Backgrounds in robotics, automotive engineering, mechanical engineering, GIS, or simulation are strong indicators.
• Time-Series Data Literacy: Experience reading and interpreting sensor data, telemetry charts, lab instrumentation output, or signal processing data. Comfort with chart-heavy analytical workflows is essential for making precise trim decisions.
• Driving Familiarity: Regular driving experience, ideally in varied or off-road conditions. Must be able to distinguish safe from unsafe driving behavior, recognize complete vs. rolling stops, and assess reasonable following distances.
• Detail Orientation with Tolerance for Ambiguity: Ability to follow precise, rule-based guidelines while also applying sound judgment on frequent edge cases. Prior experience in QA, data annotation, or lab/research settings is a strong signal.
• Video Review Endurance: Comfort with sustained video review tasks. Prior experience in video editing, surveillance monitoring, sports performance analysis, or media production is a plus.

• Prior annotation or data labeling experience, especially in autonomy or robotics datasets.
• Familiarity with geospatial tools, map interfaces, or GIS platforms.
• Hands-on experience with Encord, Label Studio, CVAT, Scale AI, or comparable labeling platforms.
• Background in autonomous vehicles, ADAS systems, or driver safety analysis.

This is a remote position.