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Anthropic Limited

Research Engineer, Societal Impacts

Anthropic Limited, San Francisco, California, United States, 94199


About the RoleAs a Research Engineer 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.

Read more about our team in our recruiting blog post.

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, 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.

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

Translate insights to inform Anthropic strategy, research, and public policy

You may be a good fit if you:

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

Have experience with distributed systems and can design for scale and reliability

Have a track record of using technical infrastructure to interface effectively with machine learning models

Have experience deriving insights from imperfect data streams

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

Scaling and optimizing the performance of tools

Representative Projects:

Design and implement scalable infrastructure for running large-scale experiments on how people interact with our AI systems

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

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

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