Engineering Manager, Research Data Platform

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.