Part-Time Genomics & Cell Biology SME for AI Lab

Mercor

United States

Remote

USD 96,000 - 138,000

Part time

14 days+

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Job summary

Mercor in the United States is seeking Genomics and Cell Biology SMEs to contribute to foundational AI model development for biology. We're seeking talented experts, including molecular biologists, cell biologists, geneticists and synthetic biologists, to bring hands-on laboratory or computational expertise to AI model design and evaluation.

This part-time role through Cincinnatus LLC offers placement at a leading AI Lab as part of the extended workforce.

Qualifications

PhD in molecular biology, cell biology, genetics, genomics, synthetic biology, biochemistry or a closely related discipline. Strong recent PhD graduates and postdoctoral researchers are encouraged to apply.
Hands-on experience (bench or computational) with workflows such as PCR / qPCR, molecular cloning, CRISPR, cell-based assays, sequence analysis or functional genomics.
Broad experience across organisms, model systems and methods, rather than a single narrow technique.
Comfortable using scientific software and AI tools (no coding required).
Ability to engage reliably for at least 20 hours/week during weekdays.
Strong written communication and collaboration skills, and the ability to explain complex technical decisions clearly.
Nice to have: 1 to 3 years of post-PhD experience, or experience evaluating AI models or agents, designing benchmarks, or writing grading rubrics.

Responsibilities

Guide research and engineering teams to close knowledge gaps and improve AI model performance in molecular cloning, primer and CRISPR design, functional genomics, sequence analysis and cell-based assays.
Design challenging, non-searchable biology tasks, benchmarks and training data, and write accurate, well-structured solutions to problems such as construct design, protocol planning and assay troubleshooting.
Evaluate AI model outputs and step-by-step reasoning on biology tasks, judging the biological feasibility of proposed protocols and computational workflows, and provide clear, written technical feedback.
Develop specifications, grading rubrics and verification processes for biology data produced by external technical teams, and act as the domain authority on data quality.
Collaborate with other subject matter experts to ensure consistency and accuracy in training data.