Senior Engineer, Bioinformatics

Anthropic•San Francisco, CA

6d•Hybrid

About The Position

Anthropic’s Life Sciences team builds software to accelerate biological research using AI, combining frontier models with computational and experimental research. The Senior Software Engineer will build biology software in genomics and structural biology for Anthropic’s scientists and the wider research community, and represent the team to computational biologists outside the company. The role owns the path from research code to dependable public releases, including testing, validation against reference data and established methods, benchmarking, documentation, packaging, versioning, and ongoing maintenance. The engineer will write a substantial share of the code, decide when projects are ready to ship, and maintain released software as researchers use it. They will contribute to the biology software community by maintaining public repositories, responding to issues, speaking at conferences and workshops, and bringing researchers’ needs back to the team. The team uses Claude and AI coding agents extensively, and the role is expected to use them across design, implementation, testing, and review while verifying that agent-written code meets the same standards as manually written code. The role’s focus may be genomics, structural biology, or both, and its balance between individual contribution and leading a small team is flexible. The work may expand into other areas of computational biology over time.

Requirements

Have built, released, and maintained biology software used by researchers outside the candidate’s own group, with demonstrable adoption such as public repositories, package downloads, citations, or use in other groups’ pipelines.
Have deep working expertise in genomics or structural biology, including relevant data types, file formats, standard tools, and common failure modes in at least one field.
Be proficient in Python and comfortable working with large datasets in Linux and cloud or cluster computing environments.
Practice rigorous testing and release engineering, including unit and integration tests, continuous integration, validation against reference data, packaging, and versioning.
Use AI coding tools centrally in software development and be able to demonstrate how the correctness and performance of code not written by hand are verified.
Have led a technical project with multiple contributors, setting direction and reviewing others’ work while continuing to write code.
Communicate technical work clearly to biologists and engineers, both in writing (such as documentation and tutorials) and in person (such as talks and workshops).
Have a bachelor’s degree or an equivalent combination of education, training, and/or experience.
Have a field of study relevant to the role, demonstrated through coursework, training, or professional experience.
Years of experience required correlate with the internal job-level requirements; no specific number of years is stated.

Nice To Haves

Depth in both genomics and structural biology.
A record as the creator or a core maintainer of a widely used open-source computational biology tool.
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.

Responsibilities

Design, build, and maintain biology software in genomics and structural biology, including libraries, command-line tools, pipelines, datasets, and benchmarks for internal and external researchers and AI agents.
Own the transition from research code to public release, including testing, validation against reference data and established tools, benchmarking, documentation, packaging, and deciding when software is ready to ship.
Maintain released software by triaging issues, reviewing outside contributions, and managing breaking changes.
Represent the team in the biology community through conferences, workshops, tutorials, and direct work with researchers and maintainers, and turn their needs into team priorities.
Partner with computational and experimental scientists to turn research code into well-tested, reusable software.
Lead technical direction for owned projects through design review, code review, and mentoring; as appropriate, lead a small team.
Use Claude and AI coding agents as a default part of the development process, apply the same standards to agent-written code, and establish team practices for reviewing and validating it.