What the work is
Join a leading AI lab's cutting-edge research team to be at the core of the AI revolution, where your expertise fuels the development of the most advanced LLMs.
Overview
Join a leading AI lab's cutting-edge research team and help build foundational AI models for science from the ground up. We're seeking talented Drug Discovery subject-matter experts (SMEs), including pharmacologists, DMPK / ADME scientists and toxicologists from pharma, biotech, contract research or academic settings, to bring deep, hands-on discovery expertise to the design and evaluation of advanced AI models for drug discovery.
Key Responsibilities
- Guide research and engineering teams to close knowledge gaps and improve AI model performance in mechanism of action, target and cellular pharmacology, ADME/Tox, and PK/PD.
- Design challenging, non-searchable drug discovery tasks, benchmarks and training data, and write accurate, well-structured solutions to problems such as interpreting assay readouts, identifying compound liabilities and planning optimization strategy.
- Evaluate AI model outputs and step-by-step reasoning on drug discovery tasks, diagnosing errors in scientific logic, tool usage and factual accuracy, and provide clear, written technical feedback.
- Develop specifications, grading rubrics and verification processes for drug discovery data produced by external technical teams, and act as the domain authority on data quality.
- Advise on assay design and quality control, experimental failure modes, and the biological and pharmacological validity of model outputs.
- Collaborate with other subject matter experts to ensure consistency and accuracy in training data.
Who gets hired
- PhD in pharmacology, pharmaceutical sciences, toxicology, DMPK, biochemistry or a closely related discipline, or equivalent research experience.
- 3+ years of experience in early-to-mid stage drug discovery, pharmacology, DMPK / ADME or toxicology at a recognized organization, such as a pharmaceutical or biotech company, a contract research organization, or an academic drug discovery group.
- Hands-on experience analyzing experimental drug discovery data, such as in vitro ADME and safety assays, PK/PD studies, CRO assay readouts or LC-MS bioanalysis, and using it to guide compound decisions.
- Comfortable using PK/PD or data analysis software and scientific AI tools (no coding required).
- Ability to engage reliably for at least 20 hours/week during weekdays.
- Strong written communication skills and the ability to explain complex technical decisions clearly.
- Nice to have: experience with PK/PD or ADME modeling, writing grading rubrics, designing benchmark tasks, or evaluating scientific AI models.
The platform posted this on 22 Oct 2026.