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Applied Machine Learning Engineer

Arcadia ScienceEmeryville, California, United StatesOnsite

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 ML 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

Compensation:The expected salary range is $175,000–$225,000 per year, with generous equity and benefits, depending on experience level. The position is fully on-site at our Emeryville, California lab and office. For qualified candidates, we are willing and able to sponsor visas if needed.Application Process:Interested applicants should apply using the link below and include a CV and answers to the application questions. We will review applications on a rolling basis, and the job will remain open until the position is filled.

We may not be able to respond if you don't make it past the initial application review, but beyond that we will let you know if you've advanced or not. The application process will involve a virtual fit assessment meeting, live coding exercise, an at-home ML exercise, in-person interviews with several members of our team, and reference checks.Arcadia Science is an equal opportunity workplace; we welcome people from all backgrounds and communities. We provide competitive compensation and practical benefits to keep you happy and healthy so that you can do your best work.

Please note that an offer of employment from Arcadia is contingent upon the successful clearance of a reference and background checkApply for this job

Life at Arcadia Science

A new ecosystem for scientific progress.\n\nArcadia Science is pushing the boundaries of open science and innovating at every step in the research, development, and commercialization process.
Thrive Here & What We Value1. Equal opportunity workplace2. Competitive compensation and practical benefits3. Open development environment4. Collaborative work culture5. Sustainability and efficiency focus6. Biology leveraging for real-world solutions7. Transparent communication8. Scientific innovation emphasis9. Values-driven leadership10. Commitment to open science

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