Computational Genomics AI Benchmark Expert

The work

You will create realistic computational biology problems that test whether AI systems can interpret biological data, write and run code, use scientific file formats, and produce verifiable results. You will also build expert solutions and reproducible workflows in Python.

Your work will combine scientific judgment with careful task design. Each benchmark must be scientifically correct, solvable from the supplied information, and challenging enough to distinguish reliable answers from plausible but incorrect outputs.

• Design computational genomics and bioinformatics tasks that test advanced scientific reasoning.
• Work with FASTA, FASTQ, VCF, BAM, SAM, BED, TSV, and CSV files, plus sequence annotations, expression data, and germline or somatic variant data.
• Create task specifications, input datasets, expected output schemas, and deterministic or objectively verifiable ground truths.
• Develop Python reference solutions and reproducible workflows that run in a Python environment.
• Write grading criteria, validate tasks, document deliverables, incorporate reviewer feedback, and explain technical requirements to project leads and reviewers.

What it pays and takes

This is a remote contractor assignment for 5 weeks. The listed experience level is entry level, but the role requires substantial research or equivalent experience in a relevant field.

• Pay: $260 per approved task.
• Time: 20+ hours per week.
• Location: Open to candidates in Bangladesh, Brazil, Colombia, Egypt, Ghana, India, Pakistan, Indonesia, Kenya, Nigeria, Türkiye, or Vietnam.
• Language: Professional fluency in English.
• Required: Ph.D., postdoctoral experience, or equivalent research experience in bioinformatics, computational biology, genomics, computational genetics, or a closely related field.
• Required: Strong hands-on Python programming, experience analyzing biological sequence or genomics datasets, and comfort with Linux and command-line environments.
• Required: Ability to work through messy or ambiguous data, identify artifacts, choose analytical approaches, revise assumptions, and judge when conclusions are ready for use.
• Helpful: Experience with variant analysis, transcriptomics, sequence analysis, phylogenetics, population genetics, functional genomics, or clinical genomics.
• Helpful: Familiarity with Biopython, pandas, NumPy, SciPy, samtools, bcftools, BLAST, PLINK, or equivalent tools.
• Helpful: Experience with reproducible scientific pipelines, AI or large language model evaluation, benchmark datasets, automated graders, Docker, or containerized workflows.
• Helpful: Publications involving computational genomics or bioinformatics.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training is the human work behind systems that learn from examples, including writing evaluations, checking model answers, and preparing data. Specialists are needed for scientific projects because they can judge whether an AI solution is accurate, reproducible, and meaningful in a real field. OpenTrain helps people find these projects, build a profile, and apply in one place.