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.
As a Research Engineer on the Economic Research team, you will design, build, 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 at Anthropic 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 that is clear-eyed about the economic effects of AI to help policymakers, businesses, and the public understand and navigate the transition to powerful AI. We use our insights to inform Anthropic decisions internally across the business.
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 & implementing high-quality internal infrastructure, working in a fast-paced startup environment, navigating ambiguity, and demonstrating an eagerness to develop their own research & technical skills.
Build and maintain data pipelines that process large scale Claude usage logs into canonical, reusable datasets while maintaining user privacy.
Expand privacy-preserving tools to enable new analytic functionality to support research needs.
Design and implement novel data systems leveraging language models (e.g., CLIO) where traditional software engineering patterns don't yet exist.
Develop and maintain data pipelines that are interoperable across data sources (including ingesting external data) and are designed to support economic analysis.
Contribute to the strategic development of the economic research data foundations roadmap
Ensure data reliability, integrity, and privacy compliance across all economic research data infrastructure
Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions
Create documentation and best practices that enable self-serve data access for researchers while maintaining security and governance standards.
Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission
Have experience working with Research Scientists and Economists on ambiguous AI and economic projects
Have experience with building and maintaining data infrastructure, large datasets, and internal tools in production environments.
Have experience with cloud infrastructure platforms such as AWS or GCP.
Take pride in writing clean, well-documented code in Python that others can build upon
Are comfortable making technical decisions with incomplete information while maintaining high engineering standards
Are comfortable getting up-to-speed quickly on unfamiliar codebases, and can work well with other engineers with different backgrounds across the organization
Have a track record of using technical infrastructure to interface effectively with machine learning models
Have experience deriving insights from imperfect data streams
Have experience building systems and products on top of LLMs
Have experience incubating and maturing tooling platforms used by a wide variety of stakeholders
A passion for Anthropic's mission of building helpful, honest, and harmless AI and understanding its economic implications.
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.
Strong communication skills to collaborate effectively with economists, researchers, and cross-functional partners who may have varying levels of technical expertise.
Background in econometrics, statistics, or quantitative social science research
Experience building data infrastructure and data foundations for research
Familiarity with large language models, AI systems, or ML research workflows
Prior work on projects related to labor economics, technology adoption, or economic measurement
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.
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 enor