As a Transformative AI Research Economist at Anthropic, you will build macroeconomic models of AI that could be genuinely transformative and develop the scenario-based forecasting tools that let us reason quantitatively about economic trajectories with no historical precedent.\\n\\nYou will ground projections in microeconomic signals from the Anthropic Economic Index , usage patterns across millions of real-world AI interactions, surfaced through privacy-preserving measurement , so that scenario forecasts are disciplined by what we actually observe about task transformation and productivity.\\n\\nOur team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation.\\n\\nResponsibilities:\\n\\n Build macroeconomic models of transformative AI spanning growth, labor markets, and income distribution\\n Develop and maintain scenario-based forecasting tools; publish forecasts for GDP, productivity, and unemployment under a range of AI-capability trajectories\\n Ground macroeconomic projections in microeconomic data from the Anthropic Economic Index, constraining theory with observed patterns of adoption and task transformation\\n Analyze questions of income distribution and economic governance under transformative-AI scenarios\\n Contribute to the development of AI-powered research tools for economics\\n Contribute to Economic Index Reports and publish Research Briefs on first-order questions as they arise\\n Build and maintain relationships with academic institutions, policy think tanks, and other research partners\\n Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders\\n\\nYou May Be a Good Fit If You Have:\\n\\n PhD in Economics, or an exceptional candidate close to completion\\n Background in macroeconomics, growth theory, or public finance ideally with exposure to task-based frameworks and labor economics\\n A research record that engages seriously with the possibility of transformative AI , you treat the scenarios in this posting as live questions worth modeling rigorously, not speculation to be hedged against\\n Relevant experience in some of:\\n\\n Macroeconomic modeling and structural estimation\\n Scenario-based and time-series forecasting\\n Task-based approaches to technological change\\n Computational methods, agent-based modeling, or large-scale simulation\\n Income distribution and inequality\\n Using large language models in the research workflow\\n Technical skills including:\\n\\n Proficiency in Python, Julia, or similar for computational economics\\n Facility with AI coding agents as part of a research workflow\\n Comfort learning new technical tools and frameworks\\n Demonstrated ability to:\\n\\n Lead research projects from conception to publication\\n Ship on tight timelines and revise in public as new data arrives\\n Communicate technical findings to diverse audiences\\n Strong interest in ensuring AI development benefits humanity\\n\\nSome Examples of Our Recent Work:\\n\\n Labor market impacts of AI: A new measure and early evidence\\n Anthropic Economic Index Report: Economic Primitives\\n Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption\\n Estimating AI productivity gains from Claude conversations\\n The Anthropic Economic Index\\n\\nAdditional Information:\\n\\nFor this role, we're looking for candidates who combine rigorous macroeconomic theory with computational fluency, and who are willing to model economic scenarios that fall outside the profession's usual range. The ideal candidate works at the intersection of growth theory, forecasting, and frontier AI.\\n\\nDeadline to apply: None. Applications are reviewed on a rolling basis\\n\\nThe annual compensation range for this role is listed below.\\n\\nFor 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.\\n\\nAnnual Salary: $300,000-$405,000 USD
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\nYour work will be critical to delivering a real-world validation of our approach, with a primary focus on core software engineering tasks to:
\nEnable rapid, trustworthy experimentation. Maintain rigorous benchmarking and testing. Manage scale for both data and model size. Ensure and maintain high data quality for both real-world and synthetic data.
\nKey Responsibilities
\nMinimum Qualifications
\nPreferred Qualifications
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\nThe MLS team at Freenome develops DL models using massive-scale genomic data that presents significant challenges for current training paradigms. The Senior Machine Learning Research Engineer will primarily be responsible for developing and deploying the infrastructure needed to support development of such DL models: enabling distributed DL pipelines, optimising hardware utilisation for efficient training, and performing model optimisations.
\nAs part of an interdisciplinary R&D team, they will work in close collaboration with machine learning scientists, computational biologists and software engineers to accelerate the development of state-of-the-art ML/AI models and help Freenome achieve its mission.
\nKey responsibilities include:
\nMust-haves include:
\nNice-to-haves include:
\nThe US target range of our base salary for new hires is $161,925 - $227,325. You will also be eligible to receive equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.
\nFreenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, colour, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
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