# Engineering Manager, Research Data Platform

**Company:** [Anthropic](https://hotfix.jobs/companies/anthropic)
**Location:** San Francisco, CA, New York, NY
**Role:** Engineering Management
**Salary:** $405k – $850k/yr
**Experience:** 7+ years
**Skills:** Data Pipelines, Data Modeling, Schema Design, ETL, Spark, BigQuery, ClickHouse, Duckdb, Parquet, Metrics Systems, Experiment Tracking, Dataset Management, Machine Learning, Python
**Posted:** 2026-07-07

> Lead the Research Data Platform team at Anthropic as technical lead. Set direction for data systems and canonical datasets that researchers rely on, own end-to-end pipelines and platform components, and drive adoption through close collaboration with research teams. Requires experience building scalable data platforms and setting technical direction.

## Job Description

## 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.

## Requirements
- 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: you do the 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.
- Results-oriented and pragmatic, willing to do unglamorous work when it's the highest-leverage thing.
- Excited about learning the fundamentals of machine learning research (deep ML expertise is not required).
- Care about the societal impacts of your work.
- Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field.

## Nice-to-Haves
- 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.
- 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.

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