Senior Engineer, Bioinformatics

Anthropic•San Francisco, CA

18h•$300,000 - $320,000•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 and maintain biology software for Anthropic scientists and the broader research community, with a focus on genomics, structural biology, or both. The work includes developing libraries, command-line tools, pipelines, datasets, and benchmarks that researchers can install and use on their own data, and ensuring releases are correct, validated, documented, versioned, and maintained. The role owns the path from research code to public release and ongoing support, including deciding when projects are ready to ship. It also involves representing Anthropic in the computational biology community by maintaining public repositories, responding to issues, speaking at conferences and workshops, and incorporating feedback from researchers into future work. The engineer partners with computational and experimental scientists, leads technical direction through reviews and mentoring, and may lead a small team while continuing to contribute directly. Anthropic uses Claude and AI coding agents extensively, and expects the successful candidate to use them across design, implementation, testing, and review. Agent-written code must be understood, tested, and performance-measured to the same standard as hand-written code. The position offers a flexible focus and scope that may expand across 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 the data types, file formats, standard tools, and common failure modes of 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 to verify the correctness and performance of code not written by hand.
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 and in person.
Have at least a bachelor’s degree or an equivalent combination of education, training, and experience.
Have a field of study relevant to the role, as demonstrated through coursework, training, or professional experience.

Nice To Haves

Depth in both genomics and structural biology.
A record as creator or 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 researchers and AI agents inside and outside Anthropic.
Own the process of taking research code to public release, including testing, validation against reference data and established tools, benchmarking, documentation, and packaging, and determine 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 translate their needs into team priorities.
Partner with computational and experimental scientists to turn research code into well-tested, reusable software.
Lead the technical direction of owned projects through design reviews, code reviews, and mentoring, and potentially lead a small team.
Use Claude and AI coding agents as a default part of the development process, apply rigorous review and validation to agent-written code, and establish team practices for doing so.