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Anthropic's Research Data Platform team builds internal tools that enable researchers to manage, query, and analyze data used in training and evaluating frontier AI models. The team powers applications researchers rely on daily to monitor reinforcement learning runs, explore finetuning datasets, and understand experiment internals.
In this role, you'll build data products that move data from training runs into queryable storage systems and develop the APIs, libraries, and services researchers use to manage and explore that data. Unlike typical data infrastructure positions, this role embeds closely with research workflows—you'll work directly alongside research teams, build ML-specific tooling, and leverage existing infrastructure rather than reinventing it.
Key responsibilities include:
- Building and operating data pipelines that extract training run data and land it in fast, queryable storage
- Designing and implementing APIs, libraries, and web interfaces for data management and exploration
- Developing dataset management, cataloging, and provenance tooling
- Embedding with research teams to understand workflows and identify high-leverage opportunities
- Collaborating with adjacent teams to build on existing systems
You should have significant software engineering experience, particularly with data-intensive applications or internal tooling. The ideal candidate enjoys working directly with technical users, gathers requirements iteratively, and ships products that get adopted. Prior ML or AI training experience is not required—domain learning happens quickly if you're results-oriented and flexible.
Experience with large-scale ETL, columnar storage (Spark, BigQuery, DuckDB, Parquet), time series data systems, data cataloging, ML experiment tracking, or full-stack web development is a plus. The role values engineers who can pick up slack, care about societal impact, and want to deepen their understanding of machine learning research.
