About the role

The Research Data Platform team builds the tools that Anthropic's researchers use every day to manage, query, and analyze the data that goes into training and evaluating frontier models. We power the internal applications researchers rely on to monitor RL runs, explore finetuning datasets, and understand what's happening inside their experiments.

We're looking for engineers who love working directly with users and who excel at building data products - the pipelines that move data out of training runs into queryable storage, and the APIs, libraries, and services researchers use to manage and explore it. This role sits closer to the research workflow than a typical data infrastructure position: you'll often embed with research teams, build ML-specific tooling alongside them, and leverage what our Data Infrastructure team has already built rather than reinventing it.

We do not require prior ML or AI training experience. If you enjoy working closely with technical users, learning new domains quickly, and building tools people actually want to use, you'll pick up the research context fast.
Responsibilities

• Build and operate data pipelines that extract data from research training runs and land it in storage systems that are easy and fast to query
• Work closely with researchers to design and build APIs, libraries, and web interfaces that support data management, exploration, and analysis
• Develop dataset management, data cataloging, and provenance tooling that researchers use in their day-to-day work
• Embed with research teams to understand their workflows, identify high-leverage tooling opportunities, and ship solutions quickly
• Collaborate with adjacent teams to build on existing systems rather than reinventing them

You may be a good fit if you

• Have significant software engineering experience, particularly building data-intensive applications or internal tooling
• Enjoy working directly with users, gathering requirements iteratively, and shipping things that get adopted
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with

• Large-scale ETL, columnar storage formats, and query engines (e.g., Spark, BigQuery, DuckDB, Parquet)
• High-volume time series data - ingestion, storage, and efficient querying
• Data cataloging, lineage, or metadata management systems
• ML experiment tracking or metrics platforms
• Working in environments where engineers partner closely with quantitative users - research labs, trading firms, observability or analytics startups
• Complex data visualization and full-stack web application development

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

$320,000-$405,000 USD

Logistics

Minimum education: Bachelor's 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 aren't 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 you're interested in this work. We think AI systems like the ones we're 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.

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