A Bit About Us

We are Arcadia Science, a research company founded and led by scientists. Our mission is to turn evolutionary innovations into real-world solutions by developing open, efficient, and replicable approaches to leveraging biology. We share our research as openly as possible to accelerate discovery and engage with the broader scientific community.

The Opportunity

We’re building end-to-end research workflows that apply evolutionary principles to structure and interpret biological data. Our goal is to move biology from observation and intuition toward predictive, statistical reasoning. Read more about our work through our publications.

For this role, you will develop agentic AI systems that support scientific reasoning. You’ll start by automating targeted parts of the research workflow, collaborating closely with scientists to test and improve these tools. The resulting systems will not only speed up research but also generate the data needed to train autonomous decision-making agents.

You’ll join a small team at the ground floor of this effort, working across engineering and exploratory research. Because Arcadia spans the full biological research cycle, you’ll have access to real-world feedback and iterative improvement in close partnership with researchers. We’re looking for a strong software engineer who’s excited to engage in applied ML work and fast prototyping.

Responsibilities

• Collaborating with researchers to identify and automate discrete parts of their workflows
• Building AI-driven tools to assist with planning, analysis, and decision-making
• Designing and running tests to assess automation quality and reliability
• Prototyping ML-powered agents and evaluating their performance on real tasks
• Refining systems based on hands-on feedback from researchers
• Building internal tooling to support rapid experimentation and iteration

Qualifications

• Basic understanding of the life sciences research cycle
• At least 2 years’ experience working with biological data
• Strong programming skills and ability to quickly write clean, testable code
• Experience building agentic workflows with LLMs
• Ability to understand research workflows and build tools aligned with real needs
• Strong debugging skills and comfort working in open-ended problem spaces
• Experience reading research papers and translating concepts into implementations