Proteomics Machine Learning Expert

This is a remote, project-based role for machine learning professionals with deep expertise in proteomics. You will complete tasks at the intersection of ML and proteomics — including model development, data analysis, and research tasks applied to real protein and mass spectrometry datasets. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge biotech problems, and a strong addition to your research portfolio.

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