Senior Technical Product Manager, NAPP

The New York Times•New York, NY

19h•Hybrid

About The Position

The New York Times is looking for a Senior Technical Product Manager to build content and data platforms to shape the future of how we deliver journalism to our users. The New A.I. Products and Platforms (NAPP) mission is a critical, company-wide initiative tasked with building the next generation of A.I.-powered products to accelerate The New York Times' essential subscription strategy. The mission operates across two core portfolios. A.I. Platforms is dedicated to empowering all teams across the company by building out robust platform capabilities, documentation, and guidance. A.I. Products focuses on quickly developing and scaling new A.I. prototypes and products. The New York Times has been producing work-class journalism for 175 years and we publish hundreds of new articles, recipes, videos, podcast, and other media every day. We are looking for a Senior Technical Product Manager to transform our content retrieval services. The goal is to create a modern, intelligent Discovery Engine that offers a variety of content, including video, audio, summaries, and articles. This engine will power the next generation of Times products. You will navigate deep technical complexity and high-stakes organizational dependencies. You will bridge the gap between our core internal data providers and our AI application builders to ensure our AI has Times expertise at its heart. This is a hybrid role based in our New York City office. As a Senior Product Manager, you will be a necessary member of the AI Platforms portfolio. You will oversee context engineering for AI applications at The Times, starting with the scaling and launch of a modern content retrieval product. You will be a key collaborator with other data platforms at The Times to build a new infrastructure layer for the A.I. era. And you will work with consumer product teams to advance content and user data capabilities to advance NAPP and The Times' product development lifecycle, from prototyping to production.

Requirements

5+ years of Product Management experience, with a focus on high-scale backend systems, search infrastructure, or data platforms.
Deep technical literacy in AI/ML. Demonstrated experience with vector databases (e.g., Pinecone, turbopuffer), embeddings, and RAG architectures.
Experience discussing complex technical dependencies, API design, and system boundaries with engineering partners.
Data Fluency and experience working with large-scale data pipelines (ETL/ELT) and defining metadata schemas for diverse content types.
Experience working across organizational silos (Engineering, Data Science, and Product).

Nice To Haves

A profound curiosity for journalism and the future of the information ecosystem.
Experience building digital information or education products in the LLM space, with a focus on trust, reliability, and quality in AI systems.

Responsibilities

Own the Discovery Engine Roadmap: Lead the development of the Discovery Engine from to full production migration, including deep collaboration with consumer teams on NAPP and across the company.
Cross-Functional Dependency Management: Serve as the primary partner for our Publishing, Data Platforms and the ML Function to secure clean data feeds and manage the creation and use of embeddings for text and users.
Retrieval & Ranking Strategy: Define and tune the hybrid search logic that balances semantic similarity, keyword match, and personalization signals to ensure high-quality retrieval for diverse AI-powered use cases.
Data Enrichment & Ingestion: Oversee the pipelines for AI-generated metadata – including editor-in-the-loop workflows – and ensure non-text assets like video and audio transcripts are indexed and searchable.
Validation & Performance: Define and monitor technical KPIs, including P95 query latency, throughput, retrieval accuracy, cost and downstream AI application performance using tools like Braintrust and StatSig in partnership with downstream product teams.
Vendor Evaluations: Lead technical pilots (e.g. vector databases, knowledge graphs) to de-risk architectural decisions and inform our long-term build-vs-buy strategy.
Visioning: Identify and scope opportunities for infrastructure that would provide additional context or unlock new capabilities for The Times (for example, Knowledge Graphs or semantic data layers).
Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.