Engineering Manager, Research Data Platform

About the role

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About Anthropic

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

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 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

Qualifications

• 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
• 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
• Be results-oriented and pragmatic, willing to do unglamorous work when it's the highest-leverage thing
• Be excited about learning the fundamentals of machine learning research (deep ML expertise is not required)
• Care about the societal impacts of your work

Skills

• 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, where fast-moving, exploratory data work looks a lot like research
• 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

Pay

The annual compensation range for this role is $405,000—$850,000 USD.

Schedule

We currently expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Benefits

We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Equal Opportunity Employer

We are an equal opportunity employer and consider applicants for all positions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other legally protected characteristic.