Bioinformatics Machine Learning Expert

Pay: $175/hour

This is a remote, project-based role for machine learning professionals with deep expertise in bioinformatics. You will complete tasks at the intersection of ML and biological data analysis — including model development, pipeline construction, and research tasks applied to real genomic, transcriptomic, or multi-omics 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 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)