Drug Discovery Scientist

About this role

**About the role** This is an evaluation framework for frontier AI models in drug research and development. Domain experts write realistic research problems wrapped around messy evidence worlds. The model works offline in a sandbox with open-source scientific tooling, receiving incomplete, indirect and sometimes misleading data, and must reach a defensible conclusion. The answer is present in the evidence but never stated. Your job is to build a research problem so realistic and well-constructed that the model has to genuinely reason to solve it — it can't pattern-match or look the answer up. **What you'll deliver** Three things per task: the prompt, the data room, and the grading document. 1. **Design the challenge** — identify the hidden basin and the forcing chain that makes one conclusion inevitable. 2. **Build the world** — assemble a data room from real, source-grounded data. Every decisive value traces to a real source. 3. **Write the grader** — a grading document that scores a submitted memo on substance, not style or method. 4. **Validate it** — run verification and calibration before submission. 5. **Revise it** — you own the rework if a reviewer requests changes. You are the final authority on every scientific and difficulty question in your task. **Who we're looking for** - Working expertise in mechanistic enzymology, structure-guided peptide or antibody design, fragment-based discovery, or structure-based medicinal chemistry — PhD or equivalent industry track record. - You can pose a problem whose answer you can defend rigorously, but which a strong solver cannot shortcut. - Comfortable judging reasoning quality, not just final answers. **Commitment** Minimum 20 hours per week, up to 40. **Tooling** Work happens in a browser-based studio plus Claude Code. A Claude Max subscription is required and is fully reimbursed.

Skills & domains

• ai-training
• rlhf
• sme
• annotation