Bioinformatics / Computational Genomics Expert

You will turn realistic genomics and bioinformatics problems into benchmark tasks for AI agents. Each task should require biological interpretation, multi-step computation, code execution, and results that can be checked objectively.

You will create reference solutions, test scientific correctness and difficulty, and document the work clearly. You will also incorporate reviewer feedback and communicate technical requirements or blockers.

• Design tasks using sequence, annotation, expression, and variant data.
• Define inputs, expected output formats, ground truths, and grading criteria.
• Build reproducible workflows with Python, command-line tools, and bioinformatics libraries.
• Check tasks for solvability, scientific accuracy, and resistance to plausible but incorrect answers.

This is a remote contractor assignment lasting five weeks. The engagement calls for at least 40 hours per week, including six hours per day and four hours of overlap with Pacific Time. Contractor assignments do not include medical or paid leave.

• Pay: $260 per approved task.
• Work setup: Remote contractor assignment.
• Schedule: At least 40 hours per week, including six hours per day and four hours of Pacific Time overlap.
• Education or experience: A Ph.D., postdoctoral experience, or equivalent research experience in bioinformatics, computational biology, genomics, computational genetics, or a closely related field.
• Required skills: Strong hands-on Python experience, biological sequence or genomics data analysis, and comfort with Linux and command-line environments.
• You should be able to assess ambiguous data, identify artifacts, choose suitable analytical methods, and decide when results are ready for use.
• Helpful background includes variant analysis, transcriptomics, sequence analysis, phylogenetics, population genetics, functional genomics, clinical genomics, or reproducible scientific pipelines.
• Useful tools include Biopython, pandas, NumPy, SciPy, samtools, bcftools, BLAST, PLINK, Docker, and containerized workflows.
• Additional relevant experience includes evaluating AI systems, designing benchmark datasets, creating automated graders, or publishing computational genomics research.

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

AI training is the human work behind systems that learn from examples. Experts create and review data, tasks, and model outputs so AI systems can produce more reliable results; specialized knowledge matters when the work involves fields such as genomics and bioinformatics.