Hands-on Preclinical R&D Scientist — AI-Driven Drug Discovery

Mercor; Philadelphia (Philadelphia County); On-site; USD 120,000 - 180,000; Part time

Job summary

Mercor is expanding its platform into new areas of pharmaceutical development and is seeking a hands-on preclinical R&D expert to guide AI-driven discovery. This role blends wet-lab and translational experience with data science insights.

You will review technical materials, label and annotate real-world R&D data, and provide expert input on target and modality selection, optimization, and risk, helping define data standards for the client's dataset pipeline.

Qualifications

• Hands-on preclinical R&D experience at a company that develops its own drug assets.
• Experience with one or more drug modalities (e.g., small molecules, antibodies, peptides, etc.).
• 5+ years of relevant industry R&D experience.
• Ability to translate lab data into AI model inputs and insights.
• Comfortable in a hands-on, non-administrative role within a fast-moving team.

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 target/modality selection, optimization, and risk.
• Help define structure and quality standards for the client's dataset pipeline.
• Participate in expert discussions, interviews, and research engagements.
• Interface directly with the client team.
• Contribute in a part-time engagement (~10 hours per week).

Skills

Preclinical R&D experience

Drug modalities knowledge

Interdisciplinary collaboration

Job description

Mercor is expanding its platform into new areas of pharmaceutical development and is seeking a hands-on preclinical R&D expert to guide AI-driven discovery. This role blends wet-lab and translational experience with data science insights.

You will review technical materials, label and annotate real-world R&D data, and provide expert input on target and modality selection, optimization, and risk, helping define data standards for the client's dataset pipeline.