This role leads the Research Data Platform team at Anthropic, shaping the technical direction for data systems that support AI research. The Engineering Manager works closely with researchers and engineers to build platform components, own end‑to‑end datasets, drive standardization of canonical datasets, and manage complex multi‑quarter projects. The position offers a path into people leadership, emphasizes user‑centered discovery, and values impact on societal outcomes.
Engineering Manager, Research Data Platform en Anthropic
Híbrido - San Francisco, CA; New York City, NY
Más vacantes en AnthropicSalary
USD 405,000 - 850,000
Requirements
Skills
- Built and operated data-intensive systems at scale — pipelines, storage layers, query systems — with strong instincts for data modeling and schema design that hold up as usage grows
- Set technical direction for a team, or owned the architecture of a data platform that other teams build on
- Treat internal users as customers: do discovery work, iterate with users, and measure success by adoption rather than by shipping
- Understand that researchers aren’t typical internal customers — the work is exploratory by nature, workflows differ from team to team, and requirements are discovered through experiments rather than specified up front
- Can build for that motion — keeping interfaces stable and data trustworthy while use cases change underneath you, and judging when a quick, disposable solution serves research better than a durable one
- Lead through influence — aligning engineers and stakeholders without relying on formal authority
- Are results-oriented and pragmatic, willing to do unglamorous work when it’s the highest-leverage thing
- Are excited about learning the fundamentals of machine learning research (deep ML expertise is not required)
- Care about the societal impacts of your work
- Experience with large-scale ETL and columnar or analytical storage (e.g., Spark, BigQuery, ClickHouse, DuckDB, Parquet)
- Experience with metrics or experiment-tracking systems, or high-volume time-series data
- Experience with dataset management, cataloging, or lineage tooling
- Built developer tooling or internal data platforms for demanding technical users — including in domains like quantitative trading, where fast-moving, exploratory data work looks a lot like research
- A working knowledge of machine learning
- Worked in, or closely with, an ML research lab
- Interest in — or experience with — people management and growing engineers
Responsibilities
- Work directly with researchers and the engineers supporting them to understand their workflows, identify the highest-leverage opportunities, and shape what the team builds next
- Set the technical direction for the team across our platform and our datasets
- Design and build platform components that other teams plug into — libraries, services, and interfaces such as the metrics library used by training frameworks
- Own core datasets end to end: the pipelines that produce them, the schemas that define them, and the documentation and guarantees that make researchers trust them
- Drive convergence toward canonical datasets — including the core data model for RL transcripts — that research teams standardize on
- Lead complex, multi-quarter projects that span 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
Technologies
SparkBigQueryClickHouseDuckDBParquetmetrics libraryexperiment-tracking systemstime‑series datadataset managementcataloginglineage toolingquantitative tradingmachine learningML research
Descubre si tu currículum está listo para esta vacante
Mira cómo nuestra IA puede optimizar tu currículum y aumentar tus chances en este puesto.