Technical Program Manger

Location: San Francisco, Mountain View or Seattle

Job Overview:

This Technical Program Manager (TPM) role focuses on leading large-scale data annotation programs for AI/ML research, managing the full lifecycle from planning to quality assurance and delivery. It requires bridging program management with data operations, vendor oversight, and cross-functional teams like researchers and ML engineers. The position demands 7+ years in technical PM or data/AI ops, emphasizing scalable labeling efforts.

Key Responsibilities

• Own End-to-End Program Delivery: Lead large-scale data annotation initiatives from requirements scoping and vendor selection to flawless execution, delivery, and rigorous post-mortem analysis delivering 100M+ annotations annually with 99% quality targets.
• Foster Cross-Functional Partnerships: Collaborate with AI Research leaders, scientists, engineers, and product teams to define precise data requirements, success KPIs, and annotation protocols that power next-gen models.
• Master Vendor & Workforce Ecosystems: Negotiate contracts, enforce SLAs, and optimize throughput for external vendors and internal teams, scaling operations to meet aggressive research timelines.
• Champion Quality & Scalability: Architect advanced quality control pipelines, annotation tools, and iterative feedback mechanisms to maintain dataset integrity at petabyte scale.
• Drive Tooling & Automation Innovation: Partner with engineering to enhance annotation platforms, workflows, and data pipelines leveraging automation to slash cycle times by 50%+.
• Shape Data Strategy & Governance: Pioneer best practices in privacy, security, ethics, and compliance, safeguarding sensitive data in high-stakes AI workflows.
• Deliver Actionable Insights: Establish metrics dashboards tracking cost, quality, velocity, and volume; provide executive-level reporting to influence strategic decisions.
• Accelerate Internal Adoption: Design onboarding, workflows, and change management for agentic AI tools, boosting team productivity and product integration.
• Lead Data Quality Excellence: Standardize measurement, monitoring, and improvement processes across diverse datasets and global annotation teams.
• Engage Customers & Partners: Host evaluation workshops, pilots, and feedback loops with external stakeholders to refine programs and uncover breakthrough opportunities.

Required Qualifications

• Bachelor's or Master's in Computer Science, Data Science, Machine Learning, Information Systems, or equivalent hands-on experience.
• 7+ years in technical program/project management or operations within data-intensive AI/ML environments.
• Deep expertise in ML workflows, data pipelines, and the full annotation lifecycle.
• Proven success managing enterprise-scale data labeling/collection with third-party vendors.
• Strong proficiency in big data ecosystems (e.g., Apache Spark, Databricks, Hadoop) and data warehousing.
• Advanced SQL mastery for ad-hoc analytics, forecasting, dashboards, and root-cause analysis on complex datasets.
• Exceptional organizational, analytical, and influence skills; adept at crafting SOPs and rallying cross-functional teams to hit deadlines with superior quality.
• Self-starter thriving in ambiguous, fast-paced settings independently and collaboratively.

Preferred Qualifications

• Hands-on knowledge of GPU technologies and their role in generative AI/ML training.
• Experience with Apache Spark, Delta Lake, MLflow, or similar for scalable ML ops.