Supervisor, AI-ML Data Annotation (AV/ADAS)

Role Overview

This position centers on leading a team of data annotators who prepare high-quality training data for autonomous vehicle and advanced driver-assistance systems. You will coordinate daily production across multiple annotation workflows, making sure that complex labeling tasks—such as 3D semantic segmentation, LiDAR object detection, and telemetry-based tagging—are completed accurately and on schedule. Your ability to balance operational targets with hands-on people leadership directly affects the reliability of the AI models that depend on this data.

Key Responsibilities

• Direct the day-to-day workflow of annotation teams, assigning tasks based on priority, complexity, and individual skill levels to keep production moving steadily.
• Track team output against service-level agreements and quality benchmarks, intervening quickly when bottlenecks, errors, or resourcing gaps threaten delivery deadlines.
• Act as the first escalation point for annotators facing ambiguous labeling rules, tooling failures, or unclear client requirements, resolving issues or routing them to appropriate stakeholders.
• Review annotation quality through sampling and audits, identifying recurring error patterns and providing targeted retraining or clarification sessions to reduce rework.
• Generate and maintain operational reports on productivity, accuracy, and utilization, using the data to guide staffing decisions and process adjustments.
• Support new hires and existing team members through structured onboarding and ongoing skill development, particularly around ADAS behavior taxonomies and 3D annotation tools.
• Conduct regular one-on-one performance conversations, set clear individual goals, and document progress to support promotions, corrective actions, or performance improvement plans.
• Partner with quality assurance and client-facing teams to understand evolving annotation guidelines and ensure the team applies them consistently.

Requirements & Qualifications

• At least three years of experience in a production or BPO environment, with a proven record of meeting volume and quality targets.
• Two to three years of hands-on experience with AI/ML data annotation projects, specifically in the autonomous vehicle or ADAS sector, including familiarity with sensor data such as LiDAR, radar, and camera imagery.
• Demonstrated supervisory or team-lead experience, including coaching, performance evaluation, and managing attendance or productivity issues.
• Working knowledge of 3D semantic segmentation and bounding-box labeling, plus an understanding of ADAS behavior categories such as pedestrian actions, lane changes, and edge-case scenarios.
• Ability to interpret telemetry data and sensor logs to verify annotation correctness and identify ambiguous frames that require escalation or re-guidance.
• Strong analytical skills for root-cause analysis of quality defects and workflow inefficiencies, with experience implementing corrective actions.
• Excellent communication and documentation skills, capable of conveying detailed instructions to team members and summarizing operational updates for management.
• A bachelor’s degree is preferred, though a relevant diploma or professional certification combined with equivalent production leadership experience will be considered.