Engineering Manager, Research Data Platform

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. The Research Data Platform team builds the systems that make the enormous amounts of data generated by researchers — training runs, evaluations, RL transcripts, annotations — easy to produce, find, query, and trust.

As the team's tech lead, you'll work directly with researchers and the engineers who support them to understand their workflows, and turn what you learn into technical direction for the team, in partnership with the team's manager. A central ambition: a small set of canonical, well-documented datasets — starting with the core data model for RL — that researchers trust and standardize on. The first few months are spent close to the code and users; the role has a natural path into formal people leadership.

• Work directly with researchers and supporting engineers to understand workflows and identify highest-leverage opportunities
• Set the technical direction for the team across platform and datasets
• Design and build platform components other teams plug into — libraries, services, and interfaces such as the metrics library used by training frameworks
• Own core datasets end to end: pipelines, schemas, documentation, and guarantees
• Drive convergence toward canonical datasets, including the core data model for RL transcripts
• Lead complex, multi-quarter projects spanning several systems and teams, staying hands-on in the code
• Raise the team's technical bar through design reviews, mentorship, and the quality of your own work

• Built and operated data-intensive systems at scale — pipelines, storage layers, query systems — with strong data modeling and schema design instincts
• Set technical direction for a team, or owned the architecture of a data platform other teams build on
• Treat internal users as customers; measure success by adoption
• Understand that researchers aren't typical internal customers — exploratory work, requirements discovered through experiments
• Lead through influence; results-oriented and pragmatic
• Excited about learning ML research fundamentals (deep ML expertise not required)
• Care about the societal impacts of your work

Strong candidates may also have: experience with large-scale ETL and columnar or analytical storage (e.g., Spark, BigQuery, ClickHouse, DuckDB, Parquet); metrics or experiment-tracking systems or high-volume time-series data; dataset management, cataloging, or lineage tooling; developer tooling or internal data platforms for demanding technical users; a working knowledge of machine learning; experience in or with an ML research lab; interest in people management.

Annual salary: $405,000—$850,000 USD.

Minimum education: Bachelor's degree or equivalent combination of education, training, and/or experience. Location-based hybrid policy: all staff are expected to be in one of Anthropic's offices at least 25% of the time. Visa sponsorship is offered.

Apply via the Greenhouse posting at the provided URL.