Engineering Manager (Research Data Platform)

Join Anthropic, a leading AI safety and research company, as an Engineering Manager for the Research Data Platform team. In this role, you will work closely with researchers and engineers to understand their workflows, identify opportunities for improvement, and shape the team's technical direction. You will design and build platform components, own core datasets, and drive convergence toward canonical datasets. This position offers a competitive salary, comprehensive benefits, and the opportunity to make a significant impact in the field of AI research.

• Collaborer directement avec les chercheurs et les ingénieurs pour comprendre leurs flux de travail et identifier les opportunités d'amélioration.
• Définir la direction technique de l'équipe en matière de plateforme et de jeux de données, et concevoir des composants de plateforme.
• Diriger des projets complexes qui s'étendent sur plusieurs systèmes et équipes, tout en restant impliqué dans le code.
• 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
• Have 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
• 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
• Have set technical direction for a team, or owned the architecture of a data platform that other teams build on
• Are excited about learning the fundamentals of machine learning research (deep ML expertise is not required)
• Are results-oriented and pragmatic, willing to do unglamorous work when it's the highest-leverage thing
• Treat internal users as customers: you do the discovery work, iterate with users, and measure success by adoption rather than by shipping
• Care about the societal impacts of your work
• Lead through influence — aligning engineers and stakeholders without relying on formal authority
• 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
• We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed