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A 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  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. Key Responsibilities Produce and analyze computational workflows to interrogate and integrate protein information across non-model clades. Apply phylogenetic comparative methods, ancestral sequence reconstruction, or selection inference to large phylogenomic protein datasets across non-model lineages. Use comparative analyses of natural protein variation to inform Arcadia's organism and target selection capabilities. Troubleshoot computational challenges independently and creatively. Collaborate with cross-disciplinary teams to validate computational tools, contribute to shared research goals, and accelerate broader scientific efforts. Synthesize ideas, data, and findings into fully open-access pubs and engage with the scientific community to maximize impact and garner feedback that improves the work. Qualifications PhD or equivalent experience in evolutionary biology, molecular biology, computational biology, or a related field. At least one year of full-time relevant scientific experience post-PhD. Demonstrated expertise in developing and applying computational approaches to study protein evolution using sequences, structures, and functional data. Demonstrated expertise in molecular evolution methods, including phylogenetic comparative methods, selection inference, ancestral sequence reconstruction, or coevolutionary analysis. Familiarity with data analysis using tools like Python or R. Familiarity with biophysical or structural-biology approaches to studying protein constraint and divergence is a strong plus. Experience using protein language models or structural inference tools to study natural protein variation is a plus. Ability to align with the team to achieve organization-wide goals and organize effective collaborations. Excellent verbal and written science communication for both general and technical audiences. Compensation Successful applicants can expect to be compensated betw