BioInformatics Experts
Listing checked October 13, 2026 · pay as published by Mercor
Overview
Mercor is building training data for AI models that need to reason through real bioinformatics work. You will design multi-step analysis tasks, write original questions with one objective answer, and prepare datasets from public archives such as GEO, SRA/ENA, ArrayExpress, PRIDE, PDB, or MGnify, or from your own unpublished lab data. The work is remote and paid per hour, with a focus on reproducible results generated through code, command-line tools, and a validation Jupyter notebook in Python or R. Each task asks you to connect the analysis back to the underlying biological or translational question, including assumptions and limits in the evidence.
What You'll Do6
- 1Build multi-step bioinformatics analysis tasks that mirror realistic scientific work.
- 2Write original questions with one clear, objective answer for each task.
- 3Select and anonymize datasets from public sources such as GEO, SRA/ENA, ArrayExpress, PRIDE, PDB, MGnify, or your own unpublished lab data.
- 4Run analyses with code, scientific software, and command-line tools to produce results another researcher can reproduce.
- 5Create a Jupyter validation notebook in Python or R that reaches the same answer you set for the question.
- 6Interpret findings against the biological or translational question, spelling out assumptions, uncertainty, and conclusions.
Requirements8
- 1Master's or PhD, or equivalent research background, in bioinformatics, computational biology, genomics, statistical genetics, quantitative biology, or a related discipline.
- 2Hands-on work with sequencing and omics data, including bulk or single-cell RNA-seq, WGS/WES, variant calling, metagenomics, proteomics, or similar, from raw reads through interpretation.
- 3Comfort with tools such as samtools, bcftools, bedtools, BWA, Bowtie2, or BLAST.
- 4Ability to code in Python using pandas, Biopython, or pysam, or in R/Bioconductor with DESeq2, edgeR, limma, or GenomicRanges.
- 5Skill in writing and running Jupyter notebooks that reproduce your analysis from start to finish.
- 6Clear scientific writing that states methods, assumptions, and conclusions without ambiguity.
- 7Bonus: access to unpublished or private datasets you have the right to share.
- 8Bonus: deep knowledge in a niche area, such as a non-model organism, an unusual assay, or a specialized part of genomic interpretation.
Who Should Apply
The best match is a bioinformatics researcher with a master's or PhD who has taken sequencing or omics data from raw reads through to biological interpretation. You should be comfortable moving between command-line tools like samtools or BWA and analysis code in Python or R, then packaging the whole workflow in a Jupyter notebook. Candidates who run prebuilt pipelines and cannot explain assumptions, uncertainty, or the biological question behind a result will struggle here. The role also asks for precise scientific writing, so vague notebook descriptions or answers that need outside context often score low. If your experience centers on wet-lab work alone, or if you cannot share a dataset you have the rights to use, this engagement is less suitable.
Salary Insight
The post lists $90.00 to $110.00 per hour and gives no annual salary or project length. Pay at that level fits a contract engagement for a graduate-level specialist who can deliver reproducible omics analyses with little oversight. You may still discuss expected hours, dataset scope, and rights to share private data before you start.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$80/hour
This role
$90–$110/hr
Most Machine Learning & AI roles pay $55–$105 per hour. This role's pay falls inside that range.
Based on 508 similar roles that publish pay · 102 publish only a top rate; those count at the rate they gave
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- technical writing10%
- ai training8%
- ai evaluation8%
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Compensation
$90–110/hr
Required Skills
Application Tip
Lead with the omics data types you have handled end to end and name the tools you used, such as samtools, bcftools, bedtools, BWA, DESeq2, edgeR, or pysam. Point reviewers to a Jupyter notebook built on a public dataset that starts from raw reads and finishes with a result they can rerun. Quantify scope where you can, for example the number of samples, cohorts, or variants you analyzed.
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