About The Role
Mercor is hiring Drug Discovery Scientists (Drug Hunters) and Toxicology Experts on behalf of an AI-driven life sciences partner building advanced scientific reasoning systems for drug discovery, safety assessment, and translational pharmacology. In this role, you will apply deep expertise in drug discovery or toxicology to review, annotate, and validate scientific datasets and reasoning used to train next-generation biomedical AI models.
This role bridges real-world therapeutic discovery and safety science with applied AI, ensuring that complex biological mechanisms, pharmacological tradeoffs, and safety risk interpretations are accurately represented in model training.
Key Responsibilities
Scientific Data Annotation
- Review and annotate datasets related to drug discovery, pharmacology, and safety biology.
- Interpret experimental outputs from target biology studies, biochemical assays, cellular models, and safety screens.
- Analyze relationships between compound structure, potency, selectivity, exposure, and toxicity signals.
- Identify mechanistic explanations behind efficacy or toxicity findings across discovery experiments.
- Distinguish meaningful biological signal from experimental artifacts, assay interference, or model limitations.
Quality Review & Validation
- Audit annotated scientific datasets for biological, pharmacological, and safety accuracy.
- Validate structure--activity relationships (SAR), target engagement logic, and pharmacokinetic interpretations.
- Evaluate AI-generated reasoning on drug mechanism, toxicity risk, and safety margins.
- Ensure correct interpretation of dose-response relationships, exposure margins, and translational relevance.
Methodology & Knowledge Contribution
- Contribute to annotation guidelines for:
- Drug discovery workflows
- Structure--activity relationships (SAR)
- Pharmacokinetics and ADME reasoning
- Toxicity mechanisms and safety pharmacology
- Translational biology and target validation
- Provide expertise on how discovery teams balance potency, selectivity, safety, and developability.
- Advise on classification of toxicity findings, safety signals, and risk assessment frameworks.
Model Evaluation & Feedback
- Review AI-generated reasoning traces involving:
- Drug mechanism of action
- Target biology interpretation
- Toxicity mechanisms and risk assessment
- Pharmacokinetic and exposure modeling
- Assess whether conclusions logically follow from experimental evidence and biological context.
- Provide structured feedback to improve scientific rigor, causal reasoning, and translational relevance in model outputs.
Documentation & Training Support
- Contribute to scientific standards documentation and training materials for model development.
- Help define gold-standard examples of drug discovery reasoning and toxicity interpretation.
- Support calibration workflows spanning pharmacology, toxicology, and translational biology domains.
Requirements
PhD, PharmD, DVM, MD, or MS with significant industry experience in:
- Medicinal Chemistry
- Pharmacology
- Toxicology
- Chemical Biology
- Molecular Biology
- Pharmaceutical Sciences
- Biochemistry
3--5 years of hands-on experience in drug discovery or safety assessment, including:
- Drug discovery programs from target validation through lead optimization
- Structure--activity relationship (SAR) analysis
- Pharmacokinetics (PK) and ADME interpretation
- Toxicology and safety pharmacology studies
- Interpretation of in vitro and in vivo experimental data
Strong expertise in:
- Target biology and mechanism-of-action reasoning
- Dose-response relationships and exposure margins
- Translational interpretation between preclinical and clinical findings
- Toxicity mechanisms such as liver toxicity, cardiovascular liabilities, genotoxicity, or reproductive toxicity
- Evaluating whether safety findings are monitorable, manageable, or program-ending
Experience reviewing primary experimental data and study reports, not only summarized conclusions.
Exceptional attention to scientific accuracy and mechanistic reasoning.
Preferred
- Experience in pharmaceutical or biotechnology drug discovery teams
- Background in lead optimization, translational biology, or nonclinical safety
- Familiarity with DMPK workflows, safety biomarkers, and regulatory toxicology considerations
- Experience contributing to cross-functional discovery teams
- Exposure to AI/ML tools applied to biomedical research
