This role sits at the intersection of product thinking and full-stack engineering. You'll work directly with researchers and engineers to deeply understand their workflows, identify bottlenecks, and rapidly ship solutions that multiply their productivity. Whether you're building human feedback interfaces for model evaluation, creating platforms for experiment orchestration, or developing novel visualization tools for understanding model behavior, your work will directly accelerate our mission to build safe, reliable AI systems.

We're looking for someone who can operate with high agency in an ambiguous environment,someone who can be dropped into a research team, quickly develop domain expertise, and independently drive impactful projects from conception to delivery.

Responsibilities:

• Build and maintain full-stack applications and infrastructure that researchers use daily to conduct experiments, collect feedback, and analyze results
• Partner closely with research teams to understand their workflows, pain points, and requirements, translating these into technical solutions
• Design intuitive interfaces and abstractions that make complex research tasks accessible and efficient
• Create reusable platforms and tools that accelerate the development of new research applications
• Rapidly prototype and iterate on solutions, gathering feedback from users and refining based on real-world usage
• Take ownership of complete product areas, from understanding user needs through design, implementation, and ongoing iteration
• Contribute to technical strategy and architectural decisions for research tooling
• Mentor other engineers and help establish best practices for research application development

Requirements:

• 5+ years of software engineering experience with a strong focus on full-stack development
• Excel at rapid iteration and shipping,you can move from concept to working prototype quickly
• Have experience building tools, platforms, or infrastructure for technical users (engineers, researchers, data scientists, analysts, etc.)
• Demonstrate high agency and ability to operate independently in ambiguous environments
• Can quickly develop deep understanding of complex technical domains
• Have strong product instincts and can identify the right problems to solve
• Are proficient with modern web technologies (React, TypeScript, Python, etc.)
• Have a track record of building user-facing applications that are actually used and loved by their target audience
• Communicate effectively with both technical and non-technical stakeholders
• Care about the societal impacts of your work and are motivated by Anthropic's mission

Strong candidates may also have:

• Experience building research tools, scientific software, or experimentation platforms
• Background in machine learning, AI research, or working closely with ML researchers
• Founded or been an early engineer at a startup, particularly one focused on developer or researcher tools
• Built open-source tools or platforms with active user communities
• Experience with data visualization, interactive interfaces, or novel interaction paradigms
• Contributed to engineering platforms or internal tooling at scale (similar to Heroku, Vercel, or other platform-as-a-service products)
• Experience leveraging AI/LLMs to build more powerful or efficient tools
• Previous work in creative tools, artist tools, or other domains requiring deep user empathy
• Domain knowledge in areas like human-computer interaction, systems safety, or AI alignment

Annual compensation range for this role is $300,000-$405,000 USD.

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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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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.

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. In addition, we are exclusively hiring in SF. We support relocation, but all hires must relocate before starting.

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

The annual compensation range for this role is listed below.

Annual Salary:

$350,000 - $500,000USD

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As a TPU Kernel Engineer, you'll be responsible for identifying and addressing performance issues across many different ML systems, including research, training, and inference. A significant portion of this work will involve designing and optimising kernels for the TPU. You will also provide feedback to researchers about how model changes impact performance.

You may be a good fit if you:

• Have significant experience optimising ML systems for TPUs, GPUs, or other accelerators
• Are results-oriented, with a bias towards flexibility and impact
• Pick up slack, even if it goes outside your job description
• Enjoy pair programming (we love to pair!)
• Want to learn more about machine learning research
• Care about the societal impacts of your work

Strong candidates may also have experience with:

• High performance, large-scale ML systems
• Designing and implementing kernels for TPUs or other ML accelerators
• Understanding accelerators at a deep level, e.g. a background in computer architecture
• ML framework internals
• Language modeling with transformers

Representative projects:

• Implement low-latency, high-throughput sampling for large language models
• Adapt existing models for low-precision inference
• Build quantitative models of system performance
• Design and implement custom collective communication algorithms
• Debug kernel performance at the assembly level

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• 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.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

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.

Come work with us!

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.

Guidance on Candidates' AI Usage:

Learn about our policy for using AI in our application process

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As a Societal Impacts research scientist on the Models Research Pod, you'll close the loop between observing Claude's behavior and improving it at the model level. You'll use observational tools like Clio to analyse real-world usage patterns and build evaluations that assess whether Claude provides safe responses aligned with its Constitution.

Responsibilities:

• Using observational tools like Clio to analyse real-world usage patterns and surface insights about how people interact with Claude.
• Building and running evaluations to assess Claude's behaviour across key dimensions of its Constitution, such as safety and quality of advice in high-stakes situations.
• Partnering closely with fine-tuning, safeguards, policy, and interpretability teams to translate research insights into model improvements.
• Generating insights about the societal impact of Anthropic's systems and using this understanding to inform company strategy, research priorities, and policy positions.
• Sharing your work through research publications and external presentations, and developing tools and frameworks that make AI systems more understandable to policymakers, academics, and civil society.

You may be a good fit if:

• You have experience working with machine learning systems and are comfortable with technical infrastructure for interfacing with models.
• You have an interest in societal impacts research; prior experience in this area is a plus but not required.
• You're adaptable and collaborative, able to take direction and contribute to team priorities rather than needing to pursue a predetermined research agenda.
• You're skilled at writing up and communicating your results, even when they're null or unexpected.
• You find it exciting to partner with colleagues across teams on large-scale projects where the whole company contributes to building and analysing AI systems.
• You have a background in machine learning, data science, or another technical field that involves generating insights from complex systems.
• You're passionate about translating research insights into actionable recommendations for improving AI systems and informing policy.
• For senior candidates: you're willing to do hands-on work while also helping shape research direction, and you may be interested in management opportunities down the line.

Some examples of our work:

• Clio: A system for privacy-preserving insights into real-world AI use
• Values in the Wild
• How People Use Claude for Support, Advice, and Companionship
• Economic Index
• How AI is Transforming Work at Anthropic
• Education Report: How University Students Use Claude
• The Capacity for Moral Self-Correction in Large Language Models
• Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviours, and Lessons Learned
• Challenges in evaluating AI systems
• Towards Measuring the Representation of Subjective Global Opinions in Language Models
• Collective Constitutional AI: Aligning a Language Model with Public Input

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