Preclinical R&D Scientist - AI-Driven Drug Discovery

Obsidian; Paris; À distance; EUR 15 000 - 30 000; Temps partiel

Résumé du poste

Mercor is a venture-backed AI lab building frontier AI platforms for chemistry, materials science, and drug discovery. They are seeking hands-on preclinical R&D experts to guide what they build and how data informs model training.

You will blend wet-lab and translational insights with AI-driven approaches, contributing to data quality standards, dataset pipelines, and collaborative client engagements.

Qualifications

• Hands-on, non-administrative preclinical R&D experience at a company that develops its own drug assets and has taken programs to the clinic.
• Experience across discovery biology, medicinal chemistry, protein/antibody engineering, computational, in vivo/translational, pharmacology/PK-PD, toxicology, or CMC/formulation.
• Hands-on experience with one or more drug modalities (e.g., small molecules, antibodies, peptides, ADCs, mRNA, etc.).
• 5+ years of relevant industry R&D experience.
• Lead optimization or IND-enabling/preclinical development experience is a plus.
• Experience at mid-sized, emerging biotech outside top-20 pharmaceutical companies is nice to have.

Responsabilités

• 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.

Connaissances

Preclinical R&D

Drug modalities

Translational science

IND-enabling knowledge

Description du poste

Mercor is a venture-backed AI lab building frontier AI platforms for chemistry, materials science, and drug discovery. They are seeking hands-on preclinical R&D experts to guide what they build and how data informs model training.

You will blend wet-lab and translational insights with AI-driven approaches, contributing to data quality standards, dataset pipelines, and collaborative client engagements.