AI TRAINER – LIFE SCIENCES

About the role

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.

Responsibilities

• Evaluate and enhance AI-generated responses in life sciences domains
• Develop challenging reasoning problems for AI training
• Author rigorous scientific content to reinforce AI capabilities in real-world scientific contexts
• Analyze complex biological data
• Design and troubleshoot experiments (experimental biology track)
• Apply computational approaches to biological problems (computational biology/bioinformatics track)

Requirements

• Master's, PhD, or PhD candidate in biology or related fields (molecular biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK (Australia & New Zealand possible)
• 3+ years of hands-on industry or academic experience in discovery environments such as pharma, biotech, or research institutes
• Must pass a 3-4 hour paid skills assessment ($240) to qualify for the project
• Fluency in English is essential
• Detail-oriented with strong written communication skills
• Familiarity with AI tools, especially large language models, to better judge and improve AI responses (advantageous but not mandatory)
• Commitment to 30-40 hours weekly
• Work is fully remote, with preference for those based in the US; UK and Canada candidates also eligible, pending confirmation

Qualifications by track

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); 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 phylogenetics
• 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 pharma or biotech lab, academic research lab, or CRO
• Depth in at least one of: molecular biology and cloning; cell culture and cell-based assays; protein biochemistry; immunology and flow cytometry; imaging and histology; microbiology or virology; in vivo and preclinical work
• Has designed and troubleshot experiments, not only executed established SOPs; can explain what the controls are for and diagnose why a run failed
• Generates and QCs primary data; can look at raw instrument output and judge whether a run is usable before anyone makes a figure from it