Proteomics Machine Learning Expert

Responsibilities - Apply machine learning techniques to proteomics data, including protein identification, quantification, and structure-function prediction tasks - Build, train, and evaluate ML models tailored to mass spectrometry and proteomic datasets - Develop predictive models using supervised and unsupervised learning approaches relevant to biological data - Optimize model performance through feature engineering, hyperparameter tuning, and domain-specific preprocessing - Document methodologies, model assumptions, and technical approaches clearly and reproducibly

Required qualifications - Published researcher with at least one first-author publication in a peer-reviewed journal - Demonstrated expertise in both machine learning and proteomics (e.g., mass spectrometry data, protein databases, or related biological datasets) - Strong problem-solving skills and ability to work independently on technical tasks - Master's or PhD in Computational Biology, Bioinformatics, Computer Science, Data Science, or a related quantitative field

Preferred qualifications - Experience with proteomics-specific tools and pipelines (e.g., MaxQuant, Proteome Discoverer, or similar) - Background in TA'ing or teaching ML, bioinformatics, or data science courses - Familiarity with protein language models or structure prediction methods (e.g., AlphaFold, ESM)

Why apply - Exceptional Pay – Project-based pay ranges from $150–$200/hour - Portfolio Building – Gain experience applying ML to frontier proteomics problems - Professional Growth – Sharpen your skills on complex, real-world biological datasets and models - Startup Exposure – Work directly with an early-stage Y Combinator-backed company, gaining hands-on experience that sets you apart - Flexible Time Commitment – Work on your schedule while tackling meaningful scientific challenges

Employment type: Contract Time commitment: 10 hours/week Location: Remote