Bioinformatics Machine Learning Expert

Responsibilities - Apply machine learning techniques to biological datasets, including genomic, transcriptomic, proteomic, or multi-omics data - Build, train, and evaluate ML models tailored to bioinformatics applications such as variant calling, gene expression analysis, or pathway modeling - Develop predictive models using supervised and unsupervised learning approaches relevant to biological systems - Design and optimize bioinformatics pipelines integrating ML components for scalable, reproducible analysis - 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 bioinformatics (e.g., sequence analysis, genomics, transcriptomics, or related biological data domains) - Strong problem-solving skills and ability to work independently on technical tasks - Master's or PhD in Bioinformatics, Computational Biology, Computer Science, Data Science, or a related quantitative field

Preferred qualifications - Experience with standard bioinformatics tools and workflows (e.g., GATK, STAR, DESeq2, Nextflow, Snakemake, or similar) - Background in TA'ing or teaching bioinformatics, ML, or computational biology courses - Familiarity with biological foundation models or large-scale omics ML frameworks (e.g., scVI, DNABERT, Enformer)

Why apply - Exceptional Pay – Project-based pay ranges from $150–$200/hour - 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 - Portfolio Building – Gain experience applying ML to frontier bioinformatics problems - Professional Growth – Sharpen your skills on complex, real-world biological datasets and models

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