About this role
Job title: Tech Lead, Research Data Platform
About the Role Anthropic’s Research Data Platform team builds the systems that make the data researchers generate and rely on easy to produce, find, query, and trust. As the team’s tech lead, you’ll work directly with researchers and the engineers who support them to understand workflows, shape what the team builds next, and drive canonical datasets such as the core RL data model.
What You'll Do
- Work directly with researchers and engineers to understand workflows, identify high-leverage opportunities, and shape the team's roadmap.
- Set the technical direction for the platform and datasets.
- Design and build platform components that other teams plug into (libraries, services, interfaces such as the metrics library).
- Own core datasets end to end: pipelines, schemas, documentation, and guarantees that make researchers trust them.
- Drive convergence toward canonical datasets, including the core data model for RL transcripts.
- Lead complex, multi-quarter projects spanning several systems and teams, while staying hands-on in code.
- Raise the team's technical bar through design reviews, mentorship, and the quality of your own work.
What We're Looking For
- Built and operated data-intensive systems at scale (pipelines, storage layers, query systems) with strong instincts for data modeling and schema design.
- Experience setting technical direction for a team or owning the architecture of a data platform used by others.
- Treat internal users as customers: discovery-driven, iterative improvements, and success measured by adoption.
- Understand researchers’ exploratory workflows; requirements discovered through experiments rather than specified up front.
- Ability to build interfaces that stay stable while use cases evolve, balancing quick, disposable solutions with durable ones.
- Lead through influence—aligning engineers and stakeholders without relying on formal authority.
- Results-oriented and pragmatic; comfortable with unglamorous but high-leverage work.
- Eager to learn the fundamentals of machine learning; deep ML expertise is not required.
- Care about the societal impacts of your work.
- Nice to have: experience with large-scale ETL and columnar/analytical storage; metrics or experiment-tracking systems; dataset management, cataloging, or lineage tooling; developer tooling or internal data platforms; ML research lab familiarity; interest in people management and growing engineers.
Nice to Have
- Experience with large-scale ETL and columnar/analytical storage
- 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
- 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
Compensation & Benefits
- Annual salary: $405,000 – $850,000 USD
- Visa sponsorship available