Biology and Bioinformatics AI Evaluation Expert

OpenTrain AI, Inc.; United States; Remote; USD 74,000 - 124,000; Part time

Job summary

OpenTrain AI, Inc. is seeking a highly skilled professional to assess biology and bioinformatics workflows used in AI benchmarking and model development.

You will analyze genomics, transcriptomics, proteomics, and related data, and document methods and best practices for technical and non-technical collaborators. You will design, evaluate, and critique workflows, analyze diverse omics datasets, review analysis pipelines, and propose improvements to validate AI models in biology.

Qualifications

• PhD or equivalent industry or research experience in relevant field.
• Hands-on biological data analysis experience.
• Proficiency in Python or R is required.

Responsibilities

• Design, evaluate, and critique biology, bioinformatics, and computational biology workflows.
• Analyze NGS, RNA-seq, single-cell, variant-calling, and protein-structure datasets.
• Review pipeline parameters, tool configurations, and statistical methods for accuracy and reproducibility.
• Recommend improvements and help validate biology-related AI models and tools.
• Explain complex scientific concepts and document findings, methodologies, and best practices.

Skills

Python

R

Statistics

Bioinformatics

Education

PhD in biology / bioinformatics / computational biology or related field

Tools

BLAST

samtools

GATK

Bioconductor

Biopython

Job description

The work

You will use specialist knowledge to assess biology and bioinformatics workflows used in AI benchmarking and model development. The work covers genomic, transcriptomic, proteomic, computational biology, and related biological data tasks.

You will explain scientific findings to both technical and non-technical collaborators and document methods, results, and recommended practices.

• Design, evaluate, and critique biology, bioinformatics, and computational biology workflows.
• Analyze NGS, RNA-seq, single-cell, variant-calling, and protein-structure datasets.
• Review pipeline parameters, tool configurations, and statistical methods for accuracy and reproducibility.
• Recommend improvements and help validate biology-related AI models and tools.
• Explain complex scientific concepts and document findings, methodologies, and best practices.

What it pays and takes

• Pay: $45 to $90 per hour.
• Work type: Contract, part-time.
• Language: Professional written and spoken English at C1 level or higher.
• Education or experience: A PhD in biology, bioinformatics, computational biology, genomics, or a related STEM field, or equivalent industry or research experience.
• Expertise: Deep knowledge in at least one area such as genomics, transcriptomics, proteomics, molecular and cell biology, systems biology, computational pharmacology, or biostatistics applied to biology.
• Technical skills: Hands-on biological data analysis, strong statistical and quantitative reasoning, and proficiency in Python and/or R.
• Relevant tools: Familiarity with BLAST, samtools, GATK, Bioconductor, or Biopython.
• Helpful background: Reproducible workflows with Nextflow or Snakemake, Linux or command-line environments, AI or machine learning applied to biology, peer-reviewed publications, patents, or open-source bioinformatics software.
• Open to candidates in: United Arab Emirates, Argentina, Austria, Bangladesh, Belgium, Bahrain, Brazil, Canada, Switzerland, Chile, Colombia, Germany, Denmark, Algeria, and Egypt.
• Open to candidates in: Spain, France, the United Kingdom, Ghana, Greece, Indonesia, India, Italy, Jordan, Japan, Kenya, Kyrgyzstan, Kuwait, Kazakhstan, Lebanon, Sri Lanka, Morocco, Mexico, and Malaysia.
• Open to candidates in: Nigeria, the Netherlands, Oman, Peru, the Philippines, Pakistan, Palestine, Portugal, Qatar, Saudi Arabia, Singapore, Thailand, Tunisia, Taiwan, the United States, Uzbekistan, Vietnam, and South Africa.

About AI training work

AI training is the human work behind better artificial intelligence. Experts review examples, test model behavior, and assess whether outputs and workflows are accurate, reproducible, and scientifically sound; specialist knowledge is valuable for complex fields such as biology and bioinformatics.