AV Labeling Program Manager II

What the Candidate Will Need / Bonus Points

What the Candidate Will Do

1. Lead complex AI/ML programs focused on 2D/3D LiDAR annotations, object detection, semantic segmentation, and other key annotation workflows.
2. Design and implement scalable data pipelines that support model training, quality control, and real-time feedback loops.
3. Partner with engineering and product teams to identify tooling gaps and deliver long-term process and technology improvements.
4. Manage global vendor operations, including RFPs, performance tracking, and quality governance to ensure best-in-class delivery.
5. Develop standard operating procedures (SOPs) for new data initiatives and guide them from pilot phase to full-scale deployment.
6. Use data to drive decisions — analyze program metrics, uncover insights, and present findings to senior leadership to influence product and business strategy.
7. Create and lead cross-functional collaboration frameworks to ensure alignment across stakeholders and smooth execution.

 Basic Qualifications

1. 7+ years of experience in program management, data operations, or ML lifecycle management, preferably in a high-growth tech or AI-driven company.
2. Proven success in managing large-scale, high-precision data programs for AI/ML systems.
3. Strong understanding of annotation workflows (LiDAR, image, video) and human-in-the-loop (HITL) model training processes.
4. Experience working cross-functionally with engineering, product, and data science teams.
5. Excellent communication and stakeholder management skills, including experience influencing at senior levels.
6. Hands-on experience working with vendors or third-party data partners, including contracting, onboarding, and performance monitoring.
7. Strong analytical mindset with the ability to turn raw data into actionable insights.

 Preferred Qualifications

1. Prior experience working at a high-growth startup, global tech company, or top-tier consulting firm.
2. Background in AI/ML operations, autonomous systems, or large-scale computer vision applications.
3. Experience building dashboards, operational tooling, or feedback loops for data quality.
4. Prior experience at a global tech company, startup, or consulting firm.