Drug Discovery Specialist, Pharmacology & ADME/Tox

Drug discovery expertise for AI

Help research and engineering teams strengthen model reasoning about mechanisms of action, targets, cellular pharmacology, ADME and toxicology, and pharmacokinetics and pharmacodynamics. The work draws on practical discovery experience from industry, contract research or academia.

Task design and scientific review

• Develop difficult drug discovery problems that cannot be answered simply by searching, together with structured, accurate reference solutions.
• Build benchmarks and training items around interpreting assays, recognizing compound liabilities and selecting optimization approaches.
• Review model answers and their reasoning for scientific logic, factual accuracy and appropriate tool use, documenting technical feedback clearly.
• Define specifications, scoring criteria and verification procedures for material produced by external technical teams, acting as the subject authority on quality.
• Advise on assay design, quality control, experimental failures and the biological or pharmacological validity of proposed answers.
• Calibrate judgments with other specialists so training data remains dependable.

Required research background

A PhD in pharmacology, pharmaceutical science, toxicology, DMPK, biochemistry or a related discipline is expected, with equivalent research experience also acceptable. At least 3 years in early or middle stage discovery, pharmacology, DMPK, ADME or toxicology is required at an established pharmaceutical or biotechnology business, contract research group or academic discovery team.

You need to have interpreted experimental data and used it to guide compound decisions. Examples include in vitro ADME and safety assays, PK/PD studies, outsourced assay results and LC MS bioanalysis.

Comfort with PK/PD or data analysis software and scientific AI tools is necessary, while coding is not required. Experience in PK/PD or ADME modeling, creating scoring criteria, designing benchmarks or evaluating scientific AI is an advantage.

Engagement and communication

This is a part time position through an employer of record partner or an appropriate international entity, with the opportunity to contribute within an AI laboratory’s extended workforce. The employment partner manages the employment relationship, payroll and benefits.

At least 20 hours of reliable weekday availability are required each week. Clear technical writing and constructive collaboration are essential, including the ability to explain complex scientific decisions in terms that other reviewers can follow.