Senior Engineer, Bioinformatics

About the role

As a Senior Software Engineer on the Life Sciences team, you'll build biology software that our own scientists and the wider research community can depend on, and you'll be one of the main people representing that work to computational biologists outside Anthropic.

The Life Sciences team works to accelerate progress in biology with AI, combining frontier models with hands-on computational and experimental research. We want the tools, pipelines, datasets, and benchmarks we build in genomics and structural biology to be useful beyond our own projects, so we plan to release them to the community. Computational biology has a long tradition of open, shared software, and we want to contribute to it well. Every release should be something a researcher can install, run on their own data, and trust: correct on real inputs, validated against reference data and established methods, documented, versioned, and maintained after launch.

You'll own that standard. You'll write a large share of the code yourself, decide when something is ready to ship, and stay with it once people start using it. You'll also be an active member of the biology software community: maintaining public repositories, answering issues, speaking at conferences and workshops, and bringing what you hear from researchers back into what we build next.

We use Claude and AI coding agents heavily to build software, and we expect you to do the same across design, implementation, testing, and review. Code written by an agent is held to the same standard as code written by hand: you should understand it, test it, and measure its performance before it ships. We're looking for someone with deep software engineering experience who already works this way.

The shape of the role is flexible. You might spend most of your time as an individual contributor, or you might lead a small group of engineers and scientists while continuing to contribute directly. Your focus can be genomics, structural biology, or both. Experience in other areas of computational biology is welcome, and we expect the work to range more widely over time.

What matters most is a record of biology software that other people have adopted and relied on.

This is a chance to shape how AI is put to work in biological research, on problems that matter for human health and scientific understanding. If building software that scientists trust is the work you want to do, we want to hear from you.

Key responsibilities

• Design, build, and maintain biology software in genomics and structural biology (libraries, command-line tools, pipelines, datasets, and benchmarks) for use by researchers and AI agents, inside Anthropic and externally

• Own the path from research code to public release, including testing, validation against reference data and established tools, benchmarking, documentation, and packaging, and make the call on when something is ready to ship

• Maintain what we release: triage issues, review outside contributions, and manage breaking changes

• Represent the team in the biology community through conferences, workshops, tutorials, and direct work with researchers and maintainers, and turn what you learn about their needs into priorities for the team

• Partner with the team's computational and experimental scientists to turn research code into well-tested, reusable software

• Lead the technical direction of the projects you own through design review, code review, and mentoring, and, depending on your interests and the team's needs, lead a small team

• Build with Claude and AI coding agents as your default way of working, hold agent-written code to the same standard as your own, and set the team's practices for reviewing and validating it

Minimum qualifications

• Have built, released, and maintained biology software that researchers outside your own group use, with evidence of adoption you can point to (for example public repositories, package downloads, citations, or use in other groups' pipelines)

• Have deep working expertise in genomics or structural biology, including the data types, file formats, standard tools, and common failure modes of at least one of these fields

• Are proficient in Python and comfortable working with large datasets in Linux and cloud or cluster computing environments

• Practice rigorous testing and release engineering: unit and integration tests, continuous integration, validation against reference data, packaging, and versioning

• Use AI coding tools as a central part of how you build software, and can show how you verify the correctness and performance of code you did not write by hand

• Have led a technical project with multiple contributors, setting direction and reviewing others' work while continuing to write code yourself

• Can explain technical work clearly to both biologists and engineers, in writing (documentation, tutorials) and in person (talks, workshops)

Preferred qualifications

• Depth in both genomics and structural biology

• A record as the creator or a core maintainer of a widely used open-source tool in computational biology

• Experience managing or formally leading a team of engineers or scientists

• A graduate degree in computational biology, bioinformatics, computer science, or a related field, or equivalent experience

• Proficiency in a systems language such as Rust, C++, or C, and experience with performance work on large biological datasets

• Experience building software in other areas of computational biology, such as phylogenetics, proteomics, cheminformatics, or high-throughput screen analysis

• Experience shipping software built largely with AI coding agents, building agentic systems on large language models, or training machine learning models on biological sequence or structure data