Proteomatics Machine Learning Engineer

Job Description • 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. 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 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) Company Description • AfterQuery is a research lab investigating the boundaries of artificial intelligence through novel datasets and experimentation. We're backed by top investors, including Y Combinator and Box Group, and support all leading AI labs.