Research Engineer, Societal Impacts

As 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.

Note: We are only open to hiring in San Francisco for this team.

• 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

• 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

• 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

• 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

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 enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.

The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.

Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.