{"html":"<p>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.</p>\n<h3>Responsibilities</h3><ul><li>Apply machine learning techniques to biological datasets, including genomic, transcriptomic, proteomic, or multi-omics data</li><li>Build, train, and evaluate ML models tailored to bioinformatics applications such as variant calling, gene expression analysis, or pathway modeling</li><li>Develop predictive models using supervised and unsupervised learning approaches relevant to biological systems</li><li>Design and optimize bioinformatics pipelines integrating ML components for scalable, reproducible analysis</li><li>Document methodologies, model assumptions, and technical approaches clearly and reproducibly</li></ul><h3>Requirements</h3><ul><li>Published researcher with at least one first-author publication in a peer-reviewed journal</li><li>Demonstrated expertise in both machine learning and bioinformatics (e.g., sequence analysis, genomics, transcriptomics, or related biological data domains)</li><li>Strong problem-solving skills and ability to work independently on technical tasks</li><li>Master&#39;s or PhD in Bioinformatics, Computational Biology, Computer Science, Data Science, or a related quantitative field</li></ul><h3>Preferred qualifications</h3><ul><li>Experience with standard bioinformatics tools and workflows (e.g., GATK, STAR, DESeq2, Nextflow, Snakemake, or similar)</li><li>Background in TA&#39;ing or teaching bioinformatics, ML, or computational biology courses</li><li>Familiarity with biological foundation models or large-scale omics ML frameworks (e.g., scVI, DNABERT, Enformer)</li></ul>"}