Engineering Manager, Research Data Platform

The shape of it

In the posting’s own words

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.

What it asks for · 9

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

Also a plus

• 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

What the job covers

• 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

Tools and skills named

Words the posting leans on

• data14×
• datasets7×
• platform7×
• research7×
• systems7×
• build6×
• researchers6×
• core5×
• users5×
• end4×
• engineers4×
• experience4×
• design3×
• internal3×
• lead3×
• people3×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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