Training Lead/Manager

Job Title

Training Lead / Training Manager LiDAR & Computer Vision (HITL Operations)

Location

India (Hybrid / Onsite as per project need)

Experience

68 years (minimum 24 years in CV / LiDAR data annotation training)

Employment Type

Full-time

Role Overview

The Training Lead / Manager will own the end-to-end training strategy, execution, and continuous capability building for LiDAR and Computer Vision annotation programs. This role is critical to ensuring annotation accuracy, productivity stability, and rapid ramp-up across projects involving 2D/3D vision data.

You will work closely with CV SMEs, QA, Operations, Tooling teams, and Clients to translate model requirements and annotation guidelines into scalable, measurable training programs .

Key Responsibilities

1. Training Strategy & Governance

• Define training frameworks for:
• LiDAR annotation (3D cuboids, polylines, point classification, segmentation)
• 2D CV annotation (bounding boxes, polygons, keypoints, segmentation)
• Design role-based learning paths for:
• Annotators (L0L2)
• Quality Analysts
• SMEs / Trainers
• Establish training governance , certification criteria, and re-certification cycles.

2. LiDAR & CV Domain Expertise

• Deep understanding of:
• LiDAR point cloud structures
• Coordinate systems, sensor fusion (Camera + LiDAR)
• Edge cases: occlusion, sparsity, overlapping objects, night/rain scenarios
• Train teams on:
• Autonomous driving datasets (roads, lanes, vehicles, pedestrians, traffic assets)
• Indoor / outdoor 3D mapping use cases
• Translate client guidelines, SOPs, and taxonomy updates into training content.

3. Training Design & Content Development

• Create and maintain:
• Training decks, videos, tool walkthroughs
• Annotation SOPs & visual decision trees
• Golden datasets & benchmark tasks
• Build error libraries and what-good-looks-like references.
• Collaborate with SMEs to update content based on:
• Client feedback
• Model error patterns
• QA trend analysis

4. Training Delivery & Execution

• Conduct:
• New joiner bootcamps
• Tool onboarding sessions (CVAT, proprietary tools, LiDAR platforms)
• SME / Trainer certification programs
• Lead OJT (On-the-Job Training) phases to stabilize:
• AHT (Average Handling Time)
• Precision, Recall, IoU scores
• Support rapid ramp-ups and pilot launches.

5. Quality & Performance Enablement

• Partner with QA teams to:
• Analyze quality defects and root causes
• Design corrective & refresher training
• Own training-linked KPIs:
• Time-to-productivity
• Post-training quality uplift
• Reduction in repeat errors
• Enable HITL best practices for complex edge cases.

6. Stakeholder & Client Collaboration

• Act as training SPOC for internal stakeholders and clients.
• Participate in:
• Client calibration calls
• Guideline walkthroughs
• Quality audits and readiness reviews
• Support presales by contributing to:
• Training approach sections in RFPs
• Capability demonstrations and POCs

7. Team Leadership & Capability Building

• Build and mentor a team of:
• Trainers
• SMEs
• Floor coaches
• Establish community of practice for CV & LiDAR expertise.
• Drive continuous learning via internal knowledge-sharing sessions.

Key Skills & Competencies

Technical / Domain

• Strong hands-on knowledge of:
• LiDAR annotation workflows
• 2D/3D CV annotation standards
• Autonomous driving datasets
• Familiarity with:
• CVAT, proprietary annotation tools
• Dataset versioning and quality metrics
• Understanding of ML lifecycle and model feedback loops.

Training & Instructional Design

• Curriculum design for technical audiences
• Adult learning principles
• Assessment design and certification frameworks

Operational & Leadership

• Strong stakeholder management
• Analytical mindset for quality & productivity trends
• Ability to scale training across large delivery teams

KPIs & Success Metrics

• Time-to-productivity for new hires
• Quality uplift post-training (Precision, Recall, IoU)
• Reduction in critical annotation errors
• Training coverage vs. ramp plan
• SME and trainer readiness scores

Preferred Qualifications

• Engineering degree (CS, ECE, AI/ML, Robotics preferred)
• Experience in autonomous driving, smart cities, or mapping projects
• Exposure to HITL-based delivery models