Research Engineer, Economic Research Data Platform

Job Application for Research Engineer, Economic Research Data Platform at Anthropic

Research Engineer, Economic Research Data Platform San Francisco, CA

About Anthropic

Anthropic’s 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

As a Research Engineer on the Economic Research Data Platform team, you will design, build, and maintain critical infrastructure that powers Anthropic's research on AI's economic impact. You will work with data systems from across Anthropic, including our research tools for privacy-preserving analysis.

The Economic Research team is part of the Anthropic Institute , and studies the economic implications of AI on individual, firm, and economy-wide outcomes. We build scalable systems to monitor AI usage patterns and directly measure the impact of AI adoption on real-world outcomes. We publish research and data, including the Anthropic Economic Index, for the benefit of the public – helping policymakers, businesses, and workers understand and navigate the transition to powerful AI. The questions we work on include: how is AI changing jobs and economic activity, who is adopting it and why, and what determines whether a region or industry captures value from it.

In this role, you will work closely with teams across Anthropic — including Data Science and Analytics, Data Infrastructure, Societal Impacts, and Public Policy — to build scalable and robust data systems that support high-leverage, high-impact research. Strong candidates will have a track record building data processing pipelines, architecting and implementing high-quality internal infrastructure, working in a fast-paced environment, and navigating ambiguity.

Responsibilities :

Build and operate the data pipelines that turn raw usage data into clean, reusable, privacy-preserving datasets

Design new systems - including developing classifiers, training probes on model internals, and building the ML pipelines behind them — for understanding how Claude is used and the impact it's having on the economy

Build self-serve workflows to ingest and integrate external data sources so they're interoperable with internal datasets

Develop the APIs, libraries, and interfaces that serve data to researchers and the public

Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic's safety mission

Contribute to the team roadmap, documentation, and practices that enable self-serve data access while maintaining safety and governance standards

Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure

You might be a good fit if you:

Have significant experience building data-intensive applications, pipelines, or internal tooling in production

Have experience with cloud infrastructure platforms such as AWS or GCP, and take pride in writing clean, well-documented code in Python that others can build upon

Have intuition for analytics workflows and empathy for how researchers and data scientists work

Are comfortable making technical decisions with incomplete information while keeping engineering standards high

Have a "full-stack mindset", not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description

Have strong communication skills to collaborate effectively with economists, researchers, and cross-functional partners who may have varying levels of technical expertise

Care about the societal impacts of your work, and are interested in AI's economic implications

Bonus qualifications:

Experience with modern data transformation, orchestration, and query frameworks

Building systems and products on top of LLMs

Privacy-preserving data systems, or data governance and lineage tooling

Building and operating web services and the infrastructure underneath them

Full-stack development or complex data visualization

Background in econometrics, statistics, or quantitative social science

Working in environments where engineers partner closely with quantitative users — research labs, trading firms, analytics companies

Some Examples of Our Recent Work

Anthropic Economic Index report: Learning curves

Labor market impacts of AI: A new measure and early evidence

Anthropic Economic Index Report: Economic Primitives

Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption

Estimating AI productivity gains from Claude conversations

The Anthropic Economic Index

Deadline to apply: None. Applications are reviewed on a rolling basis

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: $300,000 - $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…