Engineering Manager, Research Data Platform

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. The Research Data Platform team builds systems that make research data (training runs, evaluations, RL transcripts, annotations, etc.) easy to produce, find, query, and trust. The team builds platform components other systems plug into (e.g., a metrics library used by training frameworks) and owns core datasets end-to-end (e.g., pipelines behind RL transcripts).

About the role

As the team's tech lead, you will work directly with researchers and supporting engineers to understand workflows and identify high-leverage opportunities. You will translate that knowledge into technical direction for the team (in partnership with the team's manager who owns priorities and people). A central ambition is to create a small set of canonical, well-documented datasets (starting with the core data model for RL) that researchers trust and standardize on.

You will spend the first months shipping improvements in core systems, embedding with research teams, and building a map of their workflows. As the team grows, the role can evolve into formal people leadership for those who want it.

Annual Salary: $405,000 - $850,000 USD

Responsibilities

• Work directly with researchers and engineers supporting them to understand workflows, identify highest-leverage opportunities, and shape what the team builds next
• Set technical direction for the team across platform components and datasets
• Design and build platform components that other teams plug into — libraries, services, and interfaces such as metrics libraries used by training frameworks
• Own core datasets end-to-end: pipelines that produce them, schemas that define them, and the documentation and guarantees that make researchers trust them
• Drive convergence toward canonical datasets (including a core data model for RL transcripts) that research teams standardize on
• Lead complex, multi-quarter projects spanning several systems and teams, remaining hands-on in the code
• Raise the team's technical bar through design reviews, mentorship, and the quality of your own work

Requirements / Qualifications

• Experience building and operating 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
• Experience setting technical direction for a team or owning the architecture of a data platform used by other teams
• Treat internal users as customers: perform discovery work, iterate with users, and measure success by adoption rather than by shipping
• Ability to support exploratory research workflows: keep interfaces stable and data trustworthy while use cases evolve; judge when a quick disposable solution is preferable to a durable one
• Lead through influence, aligning engineers and stakeholders without relying on formal authority
• Results-oriented and pragmatic, willing to do unglamorous high-leverage work
• Interest in learning fundamentals of machine learning research (deep ML expertise not required)
• Care about societal impacts of your work

Strong candidates may also have

• Experience with large-scale ETL and columnar/analytical storage (examples listed: 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 (domains like quantitative trading called out)
• Working knowledge of machine learning or experience working in an ML research lab
• Interest in or experience with people management and growing engineers

Logistics

• Minimum education: Bachelor’s degree or equivalent combination of education, training, and/or experience
• Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
• Minimum years of experience: Correlates with internal job level requirements for the position
• Location-based hybrid policy: Staff are expected to be in one of Anthropic's offices at least 25% of the time (some roles may require more)
• Visa sponsorship: Anthropic states they do sponsor visas and will make reasonable efforts to obtain a visa if an offer is made

How we're different

Anthropic emphasizes large-scale, collaborative research projects with strong communication and cross-team collaboration. They value impact, empirical approaches to research, and frequent research discussions to pursue highest-impact work.

Benefits / Other

Anthropic offers competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office spaces for collaboration. Guidance on candidate AI usage: https://www.anthropic.com/candidate-ai-guidance