> Markdown version of [/jobs/ext/3331674-scientist-machine-learning-ai-protein-structure-data](https://www.wearedevelopers.com/jobs/ext/3331674-scientist-machine-learning-ai-protein-structure-data). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Scientist, Machine Learning AI (Protein Structure Data) - **Company:** Simulstat Inc. - **Location:** United States (Remote available) - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Bioinformatics, Computational Biology, Python (Programming Language), Machine Learning, Scientific Computating, Systems Integration, Unstructured Data - **Published:** September 14, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=73d00c04b2087f04 ## About the Role * Advanced degree (M.S. or Ph.D.) in computational biology, bioinformatics, computational chemistry, biophysics, or a related field * Hands-on experience applying machine learning to biological, chemical, or biomedical data * Experience working with protein structure data and/or structure-derived features in a research or drug discovery setting * Experience integrating or modeling phenotypic and multimodal datasets * Strong Python programming skills and experience with scientific computing and machine-learning libraries * Ability to independently execute defined project work and deliver high-quality outputs on an agreed timeline ## Description * Develop and apply machine-learning and statistical approaches to protein structure, phenotypic, and other multimodal biological datasets * Curate, harmonize, and quality-control structured and unstructured data from internal and external sources * Build reproducible computational workflows for feature generation, model training, validation, and performance evaluation * Integrate protein structural representations with phenotypic and other biological data to generate testable hypotheses and prioritize follow-up analyses * Implement clear, maintainable analysis code and contribute to shared repositories and technical documentation * Communicate methods, findings, and recommendations clearly to technical and non-technical stakeholders