Software Engineer, Research Tools

About the role

Anthropic's research teams are pushing the boundaries of AI safety and capability research, and they need exceptional tools to do their best work. As a Software Engineer on the Research Tools team, you'll build the infrastructure and applications that enable our researchers to iterate quickly, run complex experiments, and extract insights from frontier AI systems.

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.

No ML or Research experience is required

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

You may be a good fit if you

• Have 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

Representative projects

• Building interfaces for collecting and managing human feedback on model outputs at scale

• Creating experiment orchestration platforms that make it easy to launch, monitor, and analyze complex research runs

• Developing visualization tools that help researchers understand model behavior and identify failure modes

• Designing reusable components and frameworks that enable rapid development of new research applications

• Building sandboxed execution environments for safely running AI-generated code