Preclinical R&D Expert for AI-Driven Drug Discovery

Mercor; New York (NY); Remote; USD 83,000 - 138,000; Part time

Job summary

Mercor and a venture-backed AI lab are collaborating to extend a frontier AI platform into preclinical R&D for diverse drug modalities. The role blends hands-on wet-lab and translational insight with AI-driven model development, aiming to guide dataset curation and model training for next-generation medicines.

The position is hands-on, with a focus on real-world data review, data labeling, and cross-disciplinary input into target and modality decisions.

Qualifications

• Hands-on preclinical R&D experience at a company developing its own drug assets and advancing programs to the clinic.
• Experience in a core R&D function: discovery biology, medicinal chemistry, or translational pharmacology.
• Hands-on experience with one or more drug modalities (small molecules, antibodies, peptides, ADCs, etc.).
• 5+ years of relevant industry R&D experience (flexible based on depth).
• Experience at mid-sized, emerging biotech outside top-20 pharma (nice to have).
• Hands-on preclinical R&D role — not a CRO/CDMO/academic-only/clinical-development-only fit.

Responsibilities

• Review technical materials and real-world R&D data in your area of expertise.
• Label, classify, and annotate scientific data and model outputs to improve model performance.
• Provide expert perspective on key scientific and development decisions (target/modality selection, optimization, risk).
• Help define structure and quality standards for the client's dataset pipeline.
• Participate in expert discussions, interviews, and research engagements.

Skills

Hands-on preclinical R&D

Drug modalities experience

Data labeling & annotation

Industry R&D experience

Job description

Mercor and a venture-backed AI lab are collaborating to extend a frontier AI platform into preclinical R&D for diverse drug modalities. The role blends hands-on wet-lab and translational insight with AI-driven model development, aiming to guide dataset curation and model training for next-generation medicines.

The position is hands-on, with a focus on real-world data review, data labeling, and cross-disciplinary input into target and modality decisions.