Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 3
API
AWS @ 3
Communication @ 6
Compliance
Data Pipelines @ 3
Data Science
Data Visualization @ 3
GCP @ 3
LLM
Machine Learning
Python @ 3
Statistics @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
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.
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.
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
Requirements
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
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
- 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, and if an offer is made, Anthropic will make every reasonable effort to get you a visa and retains an immigration lawyer to help with this.