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

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

Logistics

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.

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As a senior individual contributor, you'll be accountable for the technical decisions that ripple across the team and beyond. You'll architect the systems, tooling, and evaluation infrastructure that determine how quickly our research can move.

Responsibilities:

• Set technical direction for evaluation systems, research infrastructure, and internal tooling across the Claude Code team

• Architect eval frameworks that measure model capabilities across diverse coding tasks and scale with our research roadmap

• Lead the design of infrastructure that enables researchers to run experiments at scale, and make the foundational tradeoffs that shape how the team operates for years

• Identify the highest-leverage engineering investments,often before anyone has asked for them,and drive them to completion

• Serve as a senior technical bridge between product and research, using strong product intuition to influence which capabilities we prioritize and how we measure progress against them

• Mentor and raise the bar for other engineers on the team; review designs, unblock peers, and model the engineering standards we want to scale

• Partner with research leads to translate ambiguous research questions into durable engineering solutions

• Own critical systems end-to-end, from architecture through production reliability, and take responsibility for their long-term health

If you have 10+ years of software engineering experience, with a track record of operating as a Staff or Principal engineer (or equivalent) at a high-caliber organization, you may be a good fit for this role.

Strong candidates may also have experience with designing or scaling eval/evaluation frameworks for ML systems, reinforcement learning infrastructure or training systems, leading technical initiatives in high-performance, demanding environments, research computing, scientific infrastructure, or developer platforms at scale, a strong quantitative foundation (math, physics, or related fields), and expertise in Python and TypeScript.

The annual compensation range for this role is $405,000-$485,000 USD.

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