Bioinformatics / Computational Genomics Expert
$260 per approved task
Verified partner opportunity
Payment Type: $150 per approved task.
We are seeking Bioinformatics and Computational Genomics experts to design challenging, agentic genomics tasks and reference solutions for GeneBench Pro.
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.
Official job description checked 2026-10-24:
About Turing
Turing is one of the world’s leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent.
Engagement Details
Commitments Required: At least 6 hours per day and a minimum of 40 hours per week, including a 4-hour overlap with PST.
Employment Type: Contractor assignment; no medical or paid leave.
Duration of Contract: 5 weeks. Expected start date is next week.
Payment Type: $150 per approved task.
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.
Key Responsibilities
• 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.
• Ensure all deliverables are well documented, reproducible, and client-ready.
• Maintain high quality and throughput while incorporating reviewer feedback.
• Communicate progress, blockers, and scientific or technical requirements to project leads and reviewers.
Qualifications Required
• 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.
Preferred Qualifications
• 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.
• Strong understanding of the experimental and biological context behind computational analyses.
Bonus Points
• Experience with AI agents or coding agents.
• Experience designing benchmark datasets or automated graders.
• Publications involving computational genomics or bioinformatics.
• Experience with Docker or containerized scientific workflows.
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