Autonomous Driving Expert
zettamine labs pvt. ltd. India
Job Description
Hello
Greetings from ZettaMine Labs Pvt Ltd!!
We are looking for Autonomous Driving Expert for PAN India.
Job Role : Autonomous Driving Expert.Location : PAN India
Notice Period : NA( As it is for Gig Workers)
Experience : 2 to 4 years
Relevant Exp : Minimum of 2 Years Experience in below list Skills
- 3D point cloud & LiDAR annotation
- Road scene geometry & lane topology
- Camera-LiDAR sensor fusion
- AV perception pipeline knowledge
- 2D/3D bounding box & segmentation
- ADAS taxonomy & driving physics
- Temporal object tracking
- Annotation QA & inter-rater agreement
Mandatory :
- Bachelor's or Master's in Computer Vision, Robotics, Electrical/Mechanical Engineering, or Physics (equivalent AV/ADAS practical experience considered)
- 2+ years in AV data annotation, ADAS validation, computer vision labeling, or robotics perception; familiarity with annotation tools (Scale AI, Labelbox, CVAT, or similar)
Job Description:
As an Autonomous Driving Expert Rater, you will precision-annotate and quality-evaluate perception data from autonomous vehicle systems — covering camera, LiDAR, and radar sensor inputs, road geometry, weather conditions, and safety-critical edge cases.You will also assess driving effect data: how vehicle perception translates into predicted or observed behaviour. Your annotations feed directly into self-driving ML training pipelines where precision has real safety consequences.
Key Responsibilities:
Perception Labeling- Annotate 2D/3D bounding boxes, segmentation masks, and object tracks across camera and LiDAR point cloud data; label static elements (lanes, signs, road geometry) and dynamic objects (vehicles, pedestrians, cyclists) with precise attribute tagging.
- Perform temporal labeling across frame sequences; apply occlusion and visibility attributes; validate sensor fusion annotations across camera, LiDAR, and radar modalities.
- Evaluate vehicle trajectories and motion planning against ground-truth behaviour; annotate scenario types (lane changes, intersections, merges, emergency stops) and environmental conditions (rain, fog, glare, night driving).
- Classify near-miss and safety-critical edge cases; flag scenarios where perception diverges from expected driving physics or safety norms.
- Maintain 98%+ accuracy; participate in inter-rater calibration; identify and escalate novel edge cases and sensor artifacts
- Leverage Platina's AI-assisted tooling (auto-labeling, active learning) to maximise throughput without compromising precision
Please provide following detail along with your updated profile to [Confidential Information]
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I will be reaching out to you over a call or email as soon as possible.
Thanks & Regards,Vyshnavi
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