Senior Technical Program Manager

NBCUniversal•New York, NY

4d•Remote

About The Position

We are seeking an experienced Technical Program Manager to lead a critical initiative building large-scale datasets for machine learning models. This role will serve as the central coordination point between our internal ML/AI, Geographic Information Systems, modeling, and technical artist teams, and external annotation partners, ensuring the successful delivery of high-quality training data that powers next-generation systems.

Requirements

Bachelor's Degree in Computer Science, Data Science, Engineering or equivalent work experience.
Fluency with project management tools such as Jira and Confluence.
Proven experience as a Technical Program Manager in AI/ML in data annotation.
Familiarity with geospatial data types (e.g., rasters, 3D point clouds, 2D vector data) and formats (COG, OSM, GeoJSON, Shapefile, LAZ) is a plus.
Familiarity with database management or administration.

Nice To Haves

Experience managing offshore, cross-time zone distributed annotation teams.
Comfort with the Unix command line and familiarity with Git for version control and a git server.
Understanding of machine learning workflows, especially supervised learning and dataset curation.
Background with real-time 3D rendering is preferred.
High attention to detail and conscientiousness.
Familiarity with AWS and ETL orchestration.
Agile or Scrum certifications.

Responsibilities

Steward the end-to-end planning, execution, and delivery of the dataset development program, including timelines, milestones, and risk mitigation.
Act as the primary liaison between ML engineers, GIS engineers, technical artists, product stakeholders, and external annotation vendors.
Manage vendor relationships, including onboarding, quality assurance, throughput tracking, and contract compliance.
Collaborate with ML and data teams to define annotation guidelines, quality metrics, and validation protocols. Ensure that merged datasets are reproducible.
Work with engineering teams to ensure annotation platforms and data pipelines are scalable, secure, and efficient.
Ensure data collection and annotation practices adhere to privacy, security, and ethical standards.
Ensure that institutional knowledge exists in written form and can be transmitted easily to onboard new team members. Work with technical stakeholders to ensure that data provenance is tracked across multiple handoffs.