Bioinformatics / Computational Genomics Expert

Role & responsibilities

• Design novel, model-challenging tasks in computational genomics and bioinformatics.
• Design tasks that test higher-order scientific judgment, including handling ambiguous or messy data, iidentifying 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, germline or somatic variant data, or other genomics artifacts.
• 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 stack.
• 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/command-line computational environments.

Preferred candidate profile

• Experience with genomics workflows such as variant analysis, transcriptomics, sequence analysis, phylogenetics, population genetics, functional genomics, or clinical genomics.
• Familiarity with common 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/LLM systems on computational scientific tasks.
• Strong understanding of experimental and biological context behind computational analyses.