Research Engineer / Scientist, Societal Impacts

About the Role

As a Research Engineer / Scientist on the Societal Impacts team, you'll design and build critical infrastructure that enables and accelerates foundational research into how our AI systems impact people and society. Your work will directly contribute to our research publications, policy campaigns, safety systems, and products.

Our team combines rigorous empirical methods with creative technical approaches. We’re currently grappling with big questions on how AI might impact the future of work, people's wellbeing, education, and more. Additionally, we are continuously studying socio-technical alignment (what values do our systems have?), and evaluating novel AI capabilities as they arise. We develop privacy-preserving tools to measure AI's effects at scale, conduct mixed-methods studies of human-AI interaction, and translate research insights into actionable recommendations for both product and policy. You can learn more about our team here

Strong candidates will have a track record of running & designing experiments relating to machine learning systems, building data processing pipelines, architecting & implementing high-quality internal infrastructure, working in a fast-paced startup environment, navigating the ambiguity inherent to novel empirical research, and demonstrating an eagerness to develop their own research & technical skills. The ideal candidate will enjoy a mixture of running experiments, developing new tools & evaluation suites, working cross-functionally across multiple research and product teams, and striving for beneficial & safe uses for AI.

For this role, we are open to a 6-month residency at Anthropic with the expectation that residents convert to full-time if they do well in their residency.

Responsibilities:

• Design and implement scalable technical infrastructure that enables researchers to efficiently run experiments and evaluate AI systems.
• Architect systems that can handle uncertain and changing requirements while maintaining high standards of reliability
• Lead technical design discussions to ensure our infrastructure can support both current needs and future research directions
• Partner closely with researchers, data scientists, policy experts, and other cross-functional partners to advance Anthropic’s safety mission
• Interface with and improve our internal technical infrastructure and tools
• Generate net-new insights about the potential societal impact of systems being developed by Anthropic
• Ship changes that help improve our models and products based on the empirical research the Societal Impacts team is conducting

You may be a good fit if you:

• Have experience working with Research Scientists on ambiguous AI projects
• Have experience building and maintaining production-grade internal tools or research infrastructure
• 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

Strong candidates may also have experience with:

• Maintaining large, foundational infrastructure
• Building simple interfaces that allow non-technical collaborators to evaluate AI systems
• Working with and prioritizing requests from a wide variety of stakeholders, including research and product teams
• Distributed systems and can design for scale and reliability
• Scaling and optimizing the performance of tools
• Product management and/or full-stack product engineering, with a track record of zero-to-one work in startup (or startup-like environments) and owning products end-to-end
• Research relating to the societal impacts of technology

Representative Projects:

• Design, implement, and mature scalable infrastructure for running large-scale experiments on how people interact with our AI systems (like Clio, Anthropic Interviewer, the Economic Index, Values in the Wild, and Emotional Impacts).
• Build robust monitoring systems that help us detect and understand potential misuse or unexpected behaviors
• Create internal tools that help researchers, policy experts, and product teams quickly analyze dynamically changing AI system characteristics