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Platform Scientist: Comparative Protein Evolution

Arcadiascience
CompanyArcadiascience
CategoryScience & Research
LocationEmeryville
RemoteOn-site (inferred)
EmploymentFull-time
LevelNot stated
SalaryUSD 130k–180k
Posted23 Mar 2026
Last verified30 Jul 2026
SourceEmployer career page (lever)
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Description
A Bit About Us:  We're 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 engage with the broader scientific community. The Opportunity: Arcadia is studying biology with evolution as our guide. Our tools, methods, and frameworks probe the tree of life to identify innovations, novelties, and organisms that accelerate our basic and translational research. We're seeking a scientist with deep experience in studying the evolution of natural protein variation using comparative approaches to substantially expand our platform. The Platform Scientist will develop computational approaches to study how natural protein variation arises and is shaped over macroevolutionary timescales, integrating sequence, structure, and function to ask how, and why, proteins differ across the tree of life. These contributions will be instrumental in developing new computational tools, validating existing tools, testing novel hypotheses, identifying knowledge and dataset gaps, and driving the creation of new computational approaches for studying protein evolution at Arcadia. We're particularly interested in candidates whose background sits at the intersection of molecular evolution and biochemistry/biophysics/structural biology. Top candidates are inventive computational evolutionary biologists with deep experience addressing evolutionary questions related to protein characteristics to unlock novel insights. They're excited to explore new and challenging datasets, familiar with developing novel analysis pipelines, and thrive in collaborative, open research environments. Enthusiasm and participation in open science are also required as we routinely share our findings via our open-source pubs.
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