Bioinformatics / Computational Genomics Expert

Crossing Hurdles; United States; À distance; USD 120 000 - 160 000; Plein temps

Résumé du poste

Crossing Hurdles is seeking a bioscience researcher to design advanced computational genomics tasks, build datasets, and define ground truths. You will leverage Python and Linux tools to create reproducible pipelines and rigorous evaluation criteria.

You will collaborate on task specifications, scoring rubrics, and client-ready deliverables, ensuring scientific validity and transparency throughout project cycles.

Qualifications

• PhD, postdoctoral experience, or equivalent research experience in bioinformatics, computational biology, genomics, or related field.
• Strong Python programming skills.
• Experience analyzing biological sequence or genomics datasets.
• Comfortable working in Linux/command-line environments.

Responsabilités

• Design novel, model-challenging tasks in computational genomics and bioinformatics.
• Create tasks testing higher-order scientific judgment and handling ambiguous data.
• Build tasks using realistic data types like FASTA/FASTQ, VCF, BAM/SAM, BED, TSV/CSV, and more.
• Develop multi-step analyses using Python, CLI tools, or established bioinformatics libraries.
• Create clear task specifications, input datasets, output schemas, and ground truths.
• Develop reproducible reference solutions and computational workflows in Python.
• Validate scientific correctness and solvability from supplied information.
• Design grading criteria to distinguish correct from plausible outputs.
• Ensure deliverables are well documented, reproducible, and client-ready.
• Communicate progress, blockers, and requirements to project leads.

Connaissances

Python

Genomics analysis

Linux/CLI

Research collaboration

Formation

PhD or postdoc in Bioinformatics/Genomics

Outils

Biopython

NumPy

Samtools

BLAST

Description du poste

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.