Computational Biologist

Arcadia Science•Emeryville, CA

1d•$150,000 - $200,000•Onsite

About The Position

We are Arcadia Science, an evolutionary biology company founded and led by scientists. Our mission is to turn natural innovations into real-world solutions by developing systematic and quantitative approaches to leveraging biology for therapeutics R&D. We share our research as openly as possible to accelerate discovery and make our work broadly useful. We're looking for several PhD-level computational scientists to help develop our bioinformatic tools into scalable products that are rigorous and useful for a wide range of researchers. You'll work closely with the platform scientists and engineering team, quickly and iteratively translating research-grade code and methods into something usable, testable, and ready to openly release. Our most urgent need right now is in protein engineering and design, but we’re also looking for expertise in genetics and phylogenomics.

Requirements

A PhD in a relevant field (computational biology, bioinformatics, computer science, or a related discipline)
At least 2 years of relevant post-PhD experience, in academia or industry
Comfortable producing production-quality code, not just analysis notebooks
A track record of creating open tools, pipelines, or software that other people used, not just running one-off analyses

Nice To Haves

Familiarity with modern protein structure and design tools (e.g., AlphaFold, ESMFold, RFdiffusion, or similar)
Track record of collaborating closely with software engineers: code review, shared repos, CI/testing workflows (rather than handing off scripts)
Experience working with agentic coding tools (e.g., Claude Code, Codex, Cursor), with intuition around trade-offs between speed, code quality, and maintainability
Experience taking a research tool through to a released product

Responsibilities

Lead or contribute to projects in areas such as protein engineering and design, phylogenetic analyses, and research protocol optimization
Improve the effectiveness or scale of computational approaches for analyzing biological data
Develop workflows to evaluate the statistical rigor of bioinformatic pipelines
Act as a bridge between scientists and engineers to turn research prototypes and analysis scripts into robust, user-facing tools
Help define what "usable" means for a given computational tool: inputs, outputs, edge cases, and the workflows real users will expect
Communicate technical tradeoffs clearly to both scientists and engineers