Research Data Platform Engineer

Anthropic; San Francisco (CA); Hybrid; USD 320,000 - 405,000; Full time

Benefits offered by this job

Visa sponsorship

Office in San Francisco

Hybrid work policy

Job summary

Anthropic is seeking an engineer for the Research Data Platform team to build data pipelines and tooling used by researchers to manage, query, and analyze data from training runs. You will embed with research teams, design APIs and libraries, and ship accessible data solutions that researchers rely on daily.

You'll collaborate with data infrastructure colleagues, work on ML-focused tooling, and contribute to end-to-end data workflows.

Qualifications

• Extensive software engineering experience building data-intensive applications.
• Experience delivering data tooling and APIs used by researchers.
• Ability to ship user-facing data products with researchers.
• Willingness to embed with research teams and learn new domains.

Responsibilities

• Build and operate data pipelines that extract data from research runs and land it in queryable storage.
• Design and build APIs, libraries, and web interfaces to support data management and analysis.
• Develop dataset management, cataloging, and provenance tooling for researchers.
• Embed with research teams to understand workflows and ship high-leverage tooling quickly.
• Collaborate with adjacent teams to extend existing systems rather than reinventing them.

Skills

Software engineering

Data pipelines

APIs and tooling

User-facing data products

Education

Bachelor’s degree or equivalent

Tools

Spark

BigQuery

DuckDB

Parquet

Job description

Anthropic is seeking an engineer for the Research Data Platform team to build data pipelines and tooling used by researchers to manage, query, and analyze data from training runs. You will embed with research teams, design APIs and libraries, and ship accessible data solutions that researchers rely on daily.

You'll collaborate with data infrastructure colleagues, work on ML-focused tooling, and contribute to end-to-end data workflows.