Technical Program Manager, Data Annotation for AI

Team EnvironmentIn this role, the nature of the work is dynamic and requires a collaborative attitude. While you have specific duties, it's important to understand that the entire team is responsible for the final delivery, and this may occasionally involve taking on additional tasks outside your primary responsibilities. The ability to adapt and contribute wherever needed is key to succeeding in this environment.‍Location Either Seattle WA or Mountain View CADuration 1 year contract that may extend Our MissionThe company is seeking a highly motivated and technically skilled Technical Program Manager (TPM) to lead and oversee data annotation programs that power our cutting-edge AI research initiatives. This role sits at the intersection of program management, data operations, and AI/ML, and will play a pivotal part in ensuring that our data annotation efforts are scalable, high-quality, and aligned with the needs of our research and product teams. You will collaborate closely with researchers, data scientists, ML engineers, and vendor operations to drive the end-to-end lifecycle of large-scale data labeling and curation efforts — from strategy and planning to execution, delivery, and quality evaluation.‍ ResponsibilitiesProgram Ownership: Drive large-scale data annotation programs end-to-end, from scoping requirements to delivery and post-mortem analysis.Cross-Functional Collaboration: Partner with AI Research leadership, AI researchers, data scientists, ML engineers, and product managers to define data needs, success metrics, and annotation guidelines.Vendor & Workforce Management: Manage external annotation vendors and internal labeling teams, including contract negotiation, SLAs, quality standards, and throughput planning.Quality & Process: Design and implement robust quality control pipelines, annotation tools, and feedback loops to ensure data quality at scale.Tooling & Automation: Collaborate with engineering to improve annotation infrastructure, workflows, and data pipelines for efficiency and scalability.Data Strategy & Governance: Contribute to data governance best practices, including privacy, security, ethics, and compliance in annotation workflows.Reporting & Metrics: Define and track key program metrics (cost, quality, speed, volume), and regularly communicate progress to stakeholders and leadership.Internal Adoption: Coordinate internal adoption of agentic AI products by building onboarding processes, workflows, and change management strategies.Data Quality Leadership: Establish and standardize processes for measuring, monitoring, and improving data quality across datasets and annotation teams.Customer Engagement: Collaborate with external customers and research partners on evaluation workshops, pilots, and feedback sessions to drive continuous improvement.‍ Competencies and Requirements- Bachelor’s or Master’s degree in a technical field (e.g. Computer Science, Data Science, Machine Learning, Information Systems) or equivalent practical experience.- 5 years of experience in technical program management, project management, or operations in data-centric or AI/ML environments.- Strong understanding of ML development workflows, data pipelines, and annotation lifecycle.- Experience managing large-scale data labeling or data collection efforts, including working with third-party vendors.- Familiarity with big data platforms (e.g. Apache Spark, Databricks, Hadoop) and data warehousing concepts.- Excellent organizational, problem-solving, and communication skills with the ability to influence cross-functional stakeholders.- Proven track record of driving cross-functional teams to deliver complex technical projects on time and with high quality.- Excellent communication, negotiation and analytical skills, with the ability to document standard operating procedures and processes- Advanced working SQL Knowledge, Ability to build and maintain analytics to track, forecast, and visualize consumption through ad-hoc SQL, reports, and dashboards- Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.- Self-motivated and able to work independently, as well as in a team environment.- Preferred good working knowledge of GPU technology and its applications in generative AI and machine learning.- Familiarity with big data technologies such as Apache Spark, Delta Lake, and MLflow is a plus.

Salary : $65 - $80