Engineering Manager, Research Data Platform

About Anthropic

Anthropics 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 the role

Anthropics 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 buildplatform 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 teams tech lead your job starts with our users. Youll 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 whats possible. Youll turn what you learn into technical direction for the team in partnership with the teams manager who owns priorities and people. A central ambition youll 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.

Youll 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.

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 teams technical bar through design reviews mentorship and the quality of your own work

You may be a good fit if you:

• 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 arent 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 its 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

Strong candidates may also have

• 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

Logistics

Minimum education: Bachelors degree or an equivalent combination of education training and/or experience

Required field of study:A field relevant to the role as demonstrated through coursework training or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently we 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.

Visa sponsorship:We do sponsor visas! However we arent able to successfully sponsor visas for every role and every candidate. But if we make you an offer we will make every reasonable effort to get you a visa and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy so we urge you not to exclude yourself prematurely and to submit an application if youre interested in this work. We think AI systems like the ones were building have enormous social and ethical implications. We think this makes representation even more important and we strive to include a range of diverse perspectives on our team.

Your safety matters to us. To protect yourself from potential scams remember that Anthropic recruiters only contact you some cases we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money fees or banking information before your first day. If youre ever unsure about a communication dont click any linksvisit for confirmed position openings.

How were different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact advancing our long-term goals of steerable trustworthy AI rather than work on smaller and more specific puzzles. We view AI research as an empirical science which has as much in common with physics and biology as with traditional efforts in computer science. Were an extremely collaborative group and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic including: GPT-3 Circuit-Based Interpretability Multimodal Neurons Scaling Laws AI & Compute Concrete Problems in AI Safety and Learning from Human Preferences.

Come work with us!

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. Guidance on Candidates AI Usage:Learn aboutour policy for using AI in our application process.