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AnthropicSan Francisco, CA | New York City, NY
Engineering Manager (Research Data Platform)
On-siteFull Time$405k - $850k per yearPosted 13 days ago
About the role
- Anthropic’s researchers generate and depend on enormous amounts of data — training runs, evaluations, RL transcripts, annotations etc.
- The Research Data Platform team builds the systems that make that data easy to produce, find, query, and trust
- We work in two modes: we build platform components that other systems plug into (for example, a metrics library that training frameworks integrate to record and retrieve run data), and we own core datasets end to end (for example, the data pipeline behind RL transcripts)
- As the team’s tech lead, your job starts with our users. You’ll work directly with researchers — and with the engineers who support them — to understand how they actually work, where managing data slows them down, and where a well-built platform component or a well-curated dataset would change what’s possible
- You’ll turn what you learn into technical direction for the team, in partnership with the team’s manager, who owns priorities and people. A central ambition you’ll drive: a small set of canonical, well-documented datasets — starting with the core data model for RL — that researchers trust and standardize on, rather than every team managing its own copies
- You’ll spend your first few months close to the code and close to users: shipping improvements in our core systems, embedding with research teams, and building your own map of their workflows. As the team grows, this role has a natural path into formal people leadership for someone who wants it
- 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 Benefits
- Comprehensive health, dental, and vision insurance for you and your dependents
- Inclusive fertility benefits via Carrot Fertility
- 22 weeks of paid parental leave
- Flexible paid time off and absence policies
- Mental health support for you and your dependents
- Competitive salary and equity packages
- Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
- Retirement plans with competitive matching
- Life and income protection plans
- $500/month flexible wellness and time saver stipend
- Commuter benefits
- Annual education stipend
- Home office stipends
- Relocation support for those moving for Anthropic
- Daily meals and snacks in the office
- 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
- Have 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
- 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
- Have set technical direction for a team, or owned the architecture of a data platform that other teams build on
- Are excited about learning the fundamentals of machine learning research (deep ML expertise is not required)
- Are results-oriented and pragmatic, willing to do unglamorous work when it’s the highest-leverage thing
- Treat internal users as customers: you do the discovery work, iterate with users, and measure success by adoption rather than by shipping
- Care about the societal impacts of your work
- Lead through influence — aligning engineers and stakeholders without relying on formal authority
- 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
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