Software Engineer, Research Data Platform na Anthropic

Híbrido - San Francisco, CA; New York City, NY

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The Research Data Platform team at Anthropic builds tools that researchers use daily to manage, query, and analyze training data, enabling monitoring of RL runs, exploration of finetuning datasets, and insight into experiments. This role focuses on building data pipelines, APIs, libraries, and web interfaces that support data management and exploration, embedding closely with research teams to deliver tools people want to use.

Salary

USD 320,000 - 405,000

Requirements

Skills

  • significant software engineering experience, particularly building data-intensive applications or internal tooling
  • enjoy working directly with users, gathering requirements iteratively, and shipping things that get adopted
  • results-oriented, with a bias towards flexibility and impact
  • pick up slack, even if it goes outside your job description
  • want to learn more about machine learning research
  • care about the societal impacts of your work
  • large-scale ETL, columnar storage formats, and query engines (e.g., Spark, BigQuery, DuckDB, Parquet)
  • high-volume time series data ingestion, storage, and efficient querying
  • data cataloging, lineage, or metadata management systems
  • ML experiment tracking or metrics platforms
  • working in environments where engineers partner closely with quantitative users (research labs, trading firms, observability or analytics startups)
  • complex data visualization and full-stack web application development
  • Bachelor’s degree or an equivalent combination of education, training, and/or experience
  • a field relevant to the role as demonstrated through coursework, training, or professional experience

Responsibilities

  • Build and operate data pipelines that extract data from research training runs and land it in storage systems that are easy and fast to query
  • Work closely with researchers to design and build APIs, libraries, and web interfaces that support data management, exploration, and analysis
  • Develop dataset management, data cataloging, and provenance tooling that researchers use in their day-to-day work
  • Embed with research teams to understand their workflows, identify high-leverage tooling opportunities, and ship solutions quickly
  • Collaborate with adjacent teams to build on existing systems rather than reinventing them

Technologies

SparkBigQueryDuckDBParquet

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