Life Sciences AI Trainer

MissionHires; United States; Remote; USD 55,000 - 172,000; Part time

Benefits offered by this job

Pay via PayPal

Fully remote contract

Flexible scheduling

Minimum 10 hours per week

Onboarding and training

Slack support and office hours

Access to Claude and ChatGPT

Job summary

MissionHires is seeking a seasoned life-sciences data analyst to design and evaluate AI-driven biology tasks, using real-world workflows and datasets.

You will run tasks through frontier AI agents, compare outputs, and develop grading rubrics for correct analyses, focusing on genomics and multiomics. Remote, part-time contract with flexible timing and paid via PayPal.

Qualifications

• At least 2 years of hands-on experience analyzing real biological data.
• Bachelor's degree in Biology or related field, in progress or higher.
• Proficiency in Python and/or R.
• Experience in genomics, transcriptomics, proteomics or multiomics.
• Ability to write, debug, and explain analysis code.
• Full professional English communication skills.
• Strong attention to detail and ability to work independently.
• Familiarity with reproducible research practices and version control.

Responsibilities

• Design challenging, realistic computational-biology tasks from real workflows.
• Run tasks through frontier AI agents and evaluate analysis outputs.
• Compare AI outputs using the same prompts and files and document results.
• Create detailed grading rubrics covering normalization and stats.
• Identify and document scientific and technical failures in analyses.
• Contribute genomics, multiomics, and machine learning expertise.
• Review and refine tasks created by other experts.
• Develop scenarios to test AI systems on life-sciences analysis.

Skills

Biology data analysis

Python

R

Genomics

Cloud workflows

Reproducible research

Statistical methods

Education

Bachelor's degree in Biology or related field

Degree in progress or higher

Tools

Python

R

Job description

When AI gets the details wrong in your field, real people pay for it. The people who know the work best have to be the ones training these models. You’re not being replaced by AI. You’re making sure it’s used accurately and safely.

Responsibilities

• Design challenging, realistic computational-biology tasks based on real-world workflows, including scenarios, colleague-style prompts, and supporting files such as count matrices, metadata, VCFs, QC outputs, annotations, notebooks, pipeline logs, and correspondence.
• Run tasks through frontier AI agents and evaluate the resulting analysis reports, annotated tables, figures, and notebooks against professional scientific standards.
• Compare multiple AI outputs using the same prompts and files, determine which performs better, and document where each response succeeds or falls short.
• Create detailed grading rubrics defining the requirements for a correct analysis, including appropriate normalization, statistical methods, multiple-testing corrections, and biological interpretation.
• Identify and document concrete scientific and technical failures, including ignored batch effects or confounders, inappropriate statistical tests, uncorrected p-values, incorrect annotation versions, dropped samples, fabricated results, misleading code, and off-brief interpretations.
• Contribute expertise across genomics and variant interpretation, bulk and single-cell transcriptomics, proteomics and multiomics, population and statistical genetics, and machine learning for biology.
• Review and refine tasks developed by other domain experts.
• Draw on personal professional experience to create scenarios that test whether AI systems can perform meaningful life-sciences analysis work.

Requirements

• At least 2 years of hands-on experience analyzing real biological data in industry, an academic lab, or a research institute, excluding undergraduate education.
• Bachelor's degree in progress or higher in Biology or a directly related field, completed in the United States, Canada, Europe, or the United Kingdom; related fields may include molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, computational biology, biostatistics, computer science, or statistics when paired with substantial biological-data experience.
• Ability to write, debug, and explain analysis code in Python and/or R; proficiency in both is preferred, and proficiency in at least one is required.
• Deep experience in at least one area: genomics and variant interpretation; bulk or single-cell transcriptomics; proteomics, metabolomics, or multiomics integration; population or statistical genetics; machine learning applied to biology; clinical genomics; metagenomics; or phylogenetics.
• Working understanding of multiple adjacent life-sciences analysis areas and the ability to assess workflows outside a primary specialization.
• Experience independently owning a multistep analysis from raw or messy data through cleaning, processing, and final interpretation.
• Familiarity with reproducible research practices, including notebooks, scripts, version control, and workflow tools.
• Strong scientific judgment regarding normalization, batch effects, confounding, multiple-testing correction, statistical power, and biological interpretation.
• Current or recent hands-on individual-contributor experience performing biological-data analysis rather than exclusively managing others.
• Full professional or native-level written and spoken English, with the ability to clearly explain complex scientific reasoning and why an analysis is incorrect.
• Strong attention to detail, comfort with ambiguity, and the ability to verify document claims against underlying data.
• Ability to interpret feedback critically, work independently, and ramp up quickly using written instructions, including incomplete materials.
• General familiarity with AI and LLM tools such as Claude or ChatGPT, including the ability to distinguish well-reasoned answers from plausible but incorrect ones.
• Comfort using cloud file tools, browser profiles, desktop applications, and common file-management tasks.
• Availability for at least 10 hours per week, with consistent availability preferred.

Benefits

• Pay range of $40–$125 USD per hour, paid through PayPal on a regular cadence.
• Fully remote contract work with flexible scheduling and no weekly maximum hours.
• Minimum commitment of 10 hours per week.
• Multi-day task timers that allow work to be completed across several days.
• Access to Claude and ChatGPT through the project; no personal subscription required.
• Paid skills assessment, onboarding, training project, task proposal, and completed work stages, subject to acceptance.
• Written feedback, revision opportunities, onboarding materials, dedicated Slack support, and office hours.
• Opportunity to contribute directly to the development of more capable and trustworthy frontier AI systems.