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 explicit 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.
Solid 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.
Solid understanding of experimental and biological context behind computational analyses.

📌 Bioinformatics / Computational Genomics Expert Gurugram (India)
🏢 Crossing Hurdles
📍 India