Bioinformatics & Computational Single-Cell Genomics Expert

$70–100/hr

50 people hired

RemoteGlobalTechnology3-5 YearsContractPosted Aug 13, 2026

About this role

You will design advanced computational bioinformatics and single-cell genomics problems for a large-scale AI evaluation benchmark. The role focuses on creating original, graduate- and research-level tasks based on realistic scientific workflows involving single-cell RNA sequencing, trajectory inference, spatial transcriptomics, multi-omic analysis, and topological data analysis. You will develop computational setups, test tasks against state-of-the-art AI models, and refine them until they achieve the required level of reasoning difficulty and scientific rigor.

Responsibilities

Scientific problem design: Create original research-level computational problems in bioinformatics and single-cell genomics.
Single-cell analysis: Develop tasks involving cell-type annotation, differential analysis, trajectory inference, pseudotime ordering, and related scRNA-seq workflows.
Spatial genomics: Build problems involving spatial transcriptomics, spatially variable gene identification, and tissue-level genomic analysis.
Multi-omic analysis: Design tasks requiring integration and interpretation of multiple molecular data modalities.
Topological analysis: Develop persistence-based and topological data-analysis workflows where relevant.
Scientific software workflows: Build tasks using tools such as scanpy, scvelo, squidpy, gudhi, and comparable specialized bioinformatics libraries.
Python development: Write computational setups, oracle functions, automated validators, and solution-checking scripts.
AI benchmark testing: Test problems against advanced AI models and refine tasks based on model performance.
Scientific validation: Verify computational results, biological assumptions, numerical outputs, edge cases, and workflow reproducibility.

Qualifications

Master’s, PhD, or equivalent research experience in Bioinformatics, Computational Biology, Genomics, Systems Biology, Biomedical Engineering, Computer Science, Statistics, or a closely related STEM discipline.
Demonstrated hands-on experience using specialized scientific software such as scanpy, scvelo, squidpy, gudhi, or comparable bioinformatics tools.
Proven proficiency demonstrated through research publications, open-source contributions, academic research, or professional scientific work.
Strong knowledge of single-cell RNA-seq analysis, cell-type annotation, trajectory inference, pseudotime, spatial transcriptomics, multi-omic integration, or related computational genomics workflows.
Strong Python programming skills for scientific analysis, task setup, oracle functions, and solution validation.
Ability to understand software limitations, edge cases, numerical behavior, and biological interpretation challenges.
Comfortable working in Linux, terminal environments, and remote computational sandboxes.
Ability to independently design, test, debug, and refine technically challenging scientific problems.