
Machine Learning Bioinformatics Expert - Remote Contract
Listing checked September 22, 2026 · pay as published by AfterQuery
Overview
AfterQuery seeks a bioinformatics machine learning expert for remote contract projects that pair machine learning with genomics, transcriptomics, and multi-omics data. Assignments arrive project by project over the next 2 to 3 weeks, with an expected 10 to 20 hours per week for work you accept. You will build models, assemble reproducible pipelines, and document methods for biological datasets such as sequence, expression, and proteomic data. The company is an early-stage Y Combinator-backed startup, and the role pays $150 to $200 per hour.
What You'll Do5
- 1Apply machine learning methods to genomic, transcriptomic, proteomic, and multi-omics datasets.
- 2Train, test, and tune models for variant calling, gene expression analysis, and pathway modeling.
- 3Build predictive models with supervised and unsupervised learning for biological systems.
- 4Design bioinformatics pipelines that include ML steps and support reproducible, scalable analysis.
- 5Write documentation that covers methodologies, model assumptions, and technical decisions.
Requirements7
- 1First-author publication in a peer-reviewed journal.
- 2Master's or PhD in bioinformatics, computational biology, computer science, data science, or another quantitative field.
- 3Proven skill in machine learning and bioinformatics, with work spanning sequence analysis, genomics, transcriptomics, or similar biological data.
- 4Ability to solve technical problems with little supervision.
- 5Familiarity with bioinformatics tools and workflows such as GATK, STAR, DESeq2, Nextflow, or Snakemake.
- 6Teaching or TA experience in bioinformatics, machine learning, or computational biology.
- 7Exposure to biological foundation models or large-scale omics ML frameworks such as scVI, DNABERT, or Enformer.
Who Should Apply
Candidates who hold a master's or PhD in a quantitative field and have at least one first-author peer-reviewed publication will match the core profile. The strongest applicants pair hands-on machine learning work with biological data experience in genomics, transcriptomics, or multi-omics, and they can point to pipelines built with tools such as Nextflow or Snakemake. This role suits independent researchers who enjoy short, focused contract projects and can commit 10 to 20 hours per week. People without a publication record, or whose ML background stays theoretical and lacks biological datasets, often score lower. A weak fit also appears when applicants cannot list programming languages, skip the GitHub profile, or submit a cover letter that does not connect their bioinformatics work to model development.
Salary Insight
Project-based pay runs from $150 to $200 per hour. That range sits well above typical academic postdoc or staff bioinformatics rates, and it aligns with specialized machine learning plus bioinformatics domain expertise rather than general data analysis work. The contract pays by project, so total earnings depend on how many assignments you accept and complete.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$75/hour
This role
$150–$200/hr
Most Machine Learning & AI roles pay $55–$100 per hour. This role's pay sits above that range.
Based on 543 similar roles that publish pay · 93 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
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- Listed in last 30 days
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Hiring most right now: micro1 (270) · Mercor (90) · SME Careers (63)
Most requested skills · share of roles
- python17%
- technical writing8%
- llm evaluation8%
- data annotation7%
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Compensation
$150–200/hr
Required Skills
Application Tip
List programming languages in a dedicated section, attach a portfolio link, and pair each language with a concrete bioinformatics or ML project. Then link the matching GitHub repository so reviewers can see commits, model code, or pipeline definitions, and in the cover letter, name your first-author publication and connect its methods to variant calling, expression analysis, or omics modeling.
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