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Bioinformatics / Computational Genomics Expert
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About the Role
We are seeking Bioinformatics and Computational Genomics experts to design challenging, agentic genomics tasks and reference solutions for GeneBench Pro. You will create realistic computational biology problems that require an AI agent to interpret biological data, write and execute code, work across scientific files, and produce objectively verifiable outputs.
What You'll Do
- Design novel, challenging tasks in computational genomics and bioinformatics.
- Design tasks that test higher-order scientific judgment, including handling ambiguous or messy data, identifying artifacts, selecting appropriate analytical approaches, revising assumptions based on intermediate results, and determining when conclusions are decision-ready.
- Build tasks involving realistic scientific data such as FASTA/FASTQ, VCF, BAM/SAM, BED, TSV/CSV, sequence annotations, expression data, and germline or somatic variant data.
- Develop tasks requiring multi-step analysis using Python, command-line tools, or established bioinformatics libraries.
- Create clear task specifications, input datasets, expected output schemas, and deterministic or objectively verifiable ground truths.
- Develop expert reference solutions and reproducible computational workflows that run in the provided Python environment.
- Validate that tasks are scientifically correct, solvable from the supplied information, and sufficiently challenging for frontier AI models.
- Design robust grading criteria that distinguish scientifically correct solutions from superficially plausible outputs.
You're a Good Fit If You
- Required skills: PhD Biology, Bioinformatician / Computational Biologist
- Ph.D., postdoctoral experience, or equivalent research experience in Bioinformatics, Computational Biology, Genomics, Computational Genetics, or a closely related discipline.
- Strong hands-on programming experience in Python.
- Experience analyzing biological sequence or genomics datasets.
- Comfortable working in Linux and command-line computational environments.
- Experience with genomics workflows such as variant analysis, transcriptomics, sequence analysis, phylogenetics, population genetics, functional genomics, or clinical genomics.
- Familiarity with bioinformatics libraries and tools such as Biopython, pandas, NumPy, SciPy, samtools, bcftools, BLAST, PLINK, or equivalent tools.
- Experience developing reproducible scientific pipelines.
- Experience evaluating AI or LLM systems on computational scientific tasks.
Role Highlights
Pay & Payout
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Common Questions
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