AI TRAINER – LIFE SCIENCES

AI Trainer – Life Sciences

Location: San Francisco, California

Category: Technology

Salary: Apply for details

Country: United States

Employment: Direct Hire/Perm

Worksite: On-Site

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Job Description AI Trainer – Life Sciences Join a pioneering effort in AI training with a fully remote, 12-week engagement specializing in life sciences research and discovery. We’re looking for highly skilled scientists, bioinformaticians, and experimental biologists to evaluate and enhance AI-generated responses, develop challenging reasoning problems, and author rigorous scientific content – all crucial to training next-generation AI systems used in drug discovery and bioinformatics.

This role offers a unique chance to contribute directly to cutting-edge AI applications, working independently in a flexible schedule while leveraging your deep scientific expertise.

This position requires a solid background in your scientific discipline, with 3+ years of hands-on industry or academic experience in discovery environments such as pharma, biotech, or research institutes.

Your work will involve analyzing complex biological data, designing and troubleshooting experiments, or applying computational approaches–all vital in reinforcing AI capabilities in real-world scientific contexts. The ideal candidate is detail-oriented, possesses strong written communication, and has familiarity with AI tools, especially large language models, to better judge and improve AI responses.

Candidates committed to 30-40 hours weekly will be considered. The work is fully remote, with a preference for those based in the US; UK and Canada candidates are also eligible, pending confirmation.

Requirements

• Candidates must hold a Master’s, PhD, or be a PhD candidate in biology or related fields such as molecular biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology, completed in the U.S., Canada, Europe, or the UK (Australia & New Zealand possible).
• Must pass a 3-4 hour paid skills assessment ($240) to qualify for the project
• Fluency in English is essential, and prior experience working with large language models or in drug discovery can be a significant advantage but is not mandatory.

Drug Discovery

• 3+ years of hands-on experience in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit)
• Direct experience in at least one of: medicinal chemistry and SAR / lead optimization; computational chemistry or CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development
• Strong familiarity with drug discovery workflows, including those involved in how a program moves from target identification and validation, through hit finding and hit-to-lead, into lead optimization and candidate selection, and on into IND-enabling studies
• Nice to have: Experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area

Computational Biology / Bioinformatics

• 3+ years of hands-on experience analyzing real biological data (industry, an academic lab, or a research institute)

• Note: Time in an academic lab or research institute must be after undergraduate education

• Writes, debugs, and can explain their own analysis code in Python and/or R (strong proficiency preferred in both, but at least one)
• Direct experience in at least one of: genomics and variant interpretation; bulk or single-cell transcriptomics; proteomics, metabolomics, or multi-omics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetic
• Has independently owned a multi-step analysis end to end (from raw or messy data, through cleaning and processing, to a final interpretation) rather than picking up a clean dataset mid-pipeline. Familiarity with reproducible research practices (notebooks, scripts, version control, workflow tools)
• Can judge whether a result is real, and say what it means (normalization choices, batch effects and confounding, multiple-testing correction, power, and interpreting results biologically)

Experimental Biology

• 3+ years of experience designing (and running) experiments at a p…