Bioinformatics and Computational Genomics Expert
Listing checked October 1, 2026 · pay as published by Turing
Overview
Turing is building GeneBench Pro, a benchmark that pushes frontier AI agents through genuine genomics analysis. You design the problems: self-contained scientific challenges where an agent gets a short prompt, raw data files, and a standard bioinformatics stack built on Python, PLINK 2.0, and common scientific libraries. Each task demands real judgment, such as spotting artifacts in messy data, revising an approach after intermediate results, and deciding when a conclusion holds up. Reference solutions must run inside that Python environment, and every output needs to be verifiable.
What You'll Do10
- 1Design original computational genomics and bioinformatics problems that stretch frontier AI models.
- 2Build tasks around authentic data types such as FASTA/FASTQ, VCF, BAM/SAM, BED, TSV/CSV, sequence annotations, and expression datasets.
- 3Write multi-step analyses that require Python, command-line tools, or established bioinformatics packages.
- 4Specify each task start to finish: input data, prompt, expected output schema, and an objectively verifiable ground truth.
- 5Create expert reference solutions and reproducible workflows that execute in the provided Python stack.
- 6Test that every task is scientifically sound, solvable from the supplied files, and hard enough for current AI agents.
- 7Define grading criteria that separate truly correct answers from outputs that only look plausible.
- 8Document deliverables so reviewers and clients can reproduce results without follow-up questions.
- 9Meet quality and volume targets while acting on reviewer feedback.
- 10Report progress, blockers, and any scientific or technical needs to project leads.
Requirements7
- 1Ph.D., postdoctoral experience, or equivalent research background in bioinformatics, computational biology, genomics, computational genetics, or a related field.
- 2Hands-on Python programming for biological data analysis.
- 3Direct experience working with biological sequence or genomics datasets.
- 4Comfort operating in Linux and command-line computational environments.
- 5Track record building reproducible scientific pipelines.
- 6Ability to judge the experimental and biological context behind a computational result.
- 7Clear written communication for task specs, documentation, and reviewer feedback.
Who Should Apply
This role fits a computational biologist or bioinformatician who has finished a Ph.D. or postdoc and still writes Python daily. You have wrestled with real VCF, BAM, or FASTQ files, you know why a pipeline needs to be reproducible, and you can explain the biology behind a variant call or an expression result. Candidates who only run canned pipelines without questioning the data tend to struggle, and so do those whose programming experience is limited to notebooks with pre-cleaned inputs. A common reason applicants score low is that their sample tasks read like textbook exercises with tidy data instead of research-grade problems that force an agent to make decisions. Another frequent miss is submitting reference solutions that depend on packages outside the provided Python stack, which makes the task impossible to validate.
Pay and demand for Biotechnology roles
AggregatedTypical pay
$88/hour
This role
Pay not disclosed
Most Biotechnology roles pay $70–$105 per hour.
Based on 47 similar roles that publish pay · 5 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
- 53
- Listed in last 30 days
- 25
- Remote
- 96%
Hiring most right now: micro1 (17) · Mercor (13) · AfterQuery (7)
Most requested skills · share of roles
- molecular biology34%
- biochemistry30%
- genetics26%
- scientific writing25%
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Compensation
Undisclosed by Turing
Comparable roles pay
47 roles$70 – $105/ hr
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Required Skills
Application Tip
In your application, describe one genomics analysis you built end to end and name the exact tools involved, for example samtools, bcftools, Biopython, or PLINK. If you have designed benchmark datasets, automated graders, or evaluated LLM outputs on scientific tasks, say so with specifics, since those map directly to the grading and validation work here.
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