Job Description
Job DescriptionA Bit About Us
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.
The Opportunity
We’re closing the gap between biological data and biological understanding. Our Platform team builds evolution-aware statistical and ML methods that use phylogeny as structure, making inference more reliable and more generalizable. The result is shared infrastructure that supports research across Arcadia. Read more about our work through our publications.
We are seeking a Platform Fellow to join our Platform team for a 3-month fellowship. Platform uses phylogeny to guide the development of many of our statistical models, machine learning tools, and evolutionary frameworks, powering research across Arcadia. This is an ideal opportunity for scientists in the final stages of their PhD or postdoc training who want to experience industry research in an open science environment, or for researchers looking to apply their quantitative skills to evolutionary biology in new ways.
Platform Fellows will work closely with our computational teams on projects at the intersection of machine learning, statistics, and evolutionary biology. Fellows will contribute to a defined project with the goal of publishing their work openly by the end of the fellowship.
Areas of Focus
We are looking for candidates with expertise in one or more of the following areas:
- Probabilistic modeling and statistical inference- Supervised and unsupervised learning for high-dimensional biological data- Interpretability methods development for ML models- Phylogenetic inference and evolutionary modeling- Comparative and evolutionary genomics across species- Quantitative and population genetics, including human genetics- Analysis of natural selection, adaptation, and trait evolution- Statistical and machine learning approaches to quantitative geneticsWhat You'll Do
- Work with Platform team members on a defined computational project aligned with Arcadia's research goals
- Independently manage your fellowship work, troubleshooting issues as they arise
- Keep detailed documentation for all workflows, code, and analyses
- Publish results openly and share data, code, and methods by the end of the fellowship
Qualifications
- PhD in computational biology, statistics, machine learning, genetics, evolutionary biology, or a related quantitative field
- Strong programming skills (Python and/or R required; familiarity with bash and version control)
- Experience with statistical modeling, machine learning, or phylogenetic methods
- Ability to work independently while collaborating with cross-functional teams
- Strong written and verbal communication skills
Fellows will receive a competitive monthly stipend. Travel and relocation support is available for candidates relocating temporarily to the Bay Area.What We Look For
The first thing we look for is technical talent at the leading edge of a field. To us, this means independent and generative scientists doing basic research that has the potential to produce evidence challenging standard practices. This is true for all of our roles: whether someone is an individual contributor or a people manager, we want researchers pushing their field forward.
For this role in particular, we are looking for scientists who want to apply quantitative methods to biological questions across the tree of life. The ideal candidate is comfortable with ambiguity, moves quickly, and communicates clearly through writing. Our team is flexible and often needs to pivot to match the pace of development at the cutting edge of multiple fields. Writing independently is crucial since we share everything we think is valuable to the community and will help us move our research forward.
We try to develop novel approaches for every aspect of our science, and everyone on our team is expected to innovate in their domain. This is not easy, and won't be for everyone. We believe a culture where this is possible stems from how we act.
Application Process
Interested applicants should apply using the link. We expect to review applications at a monthly cadence.
Due to anticipated volume, we may not be able to respond to all applicants. If you advance beyond initial review, we will let you know.