Founding Forward Deployed Scientist - Agentic Pharma R&D

### **About iollo** iollo was founded by Daniel Gomari (PhD Computational Biology, Stanford) and Prof. Mike Snyder (Stanford Genetics, 900+ publications) after watching pharma companies spend millions and months making R&D decisions that could be computed in days. We built Quinn to fix that. Quinn is an AI scientist that runs autonomous scientific workflows and delivers high-stakes R&D decisions to Fortune 500 pharma companies. ### **The role** Deliver Quinn's science directly to pharma R&D teams and ship what you learn back into the product. Quinn delivers decision-ready science to pharma partners — target validation, translational strategy, trial design, competitive intelligence, and more. Each partner engagement starts with a hard R&D question and ends with a decision package their leadership can act on. The challenge: run Quinn and turn every deployment into product improvement. You are the bridge between the AI and the pharma teams that use it. ### **What you'll do** * Work alongside Quinn to deliver for pharma partners and present findings to their R&D leadership * Run Quinn and own the quality of what ships * Write and evaluate LLM prompts daily * Ship what you learn back into Quinn to extend its capabilities ### **What you'll need** * A PhD in a life sciences discipline (biology, chemistry, pharmacology, or related) with computational fluency * 3+ years in pharma R&D with exposure to multiple stages — not just one silo * Delivered scientific findings directly to R&D leadership or external partners * Shipped tools, pipelines, or outputs that other people actually used for decisions * Hands-on comfort with Git, Python, LLM workflows, and prompt/eval loops * A self-directed approach — you figure out what needs to happen and do it ### **You'll stand out if you** * Understand drug development from target to clinic, not just your specialty * Built something real with LLMs and can explain what worked and what didn't * Have written decision memos, not just papers ### **You might be exactly right if you're one of these** * An ex-biotech computational scientist who became a product person or operator * A scientific AI product engineer — hands-on with LLM workflows, thinks in product outcomes * A technical PM from scientific software who uses the tools and inspects outputs directly ### **Tech stack** Python, Git, LLM prompt/eval workflows, scientific data analysis. Pharma R&D domain knowledge across discovery, translational, and clinical stages. ### **First 90 days** * **First 30 days:** Deliver for a live pharma partner. Run Quinn end-to-end on a real engagement and present findings to R&D leadership. Prove you can operate independently from day one. * **First 60 days:** Own the delivery playbook. Define how partner engagements run and how deployment learnings feed back into Quinn. Ship prompt and eval improvements based on real partner feedback. * **First 90 days:** You're defining how Quinn delivers science, not just executing engagements. The team defers to you on partner delivery. ### **Why join us** * Your work directly enables scientific decisions that change how drugs get made in the world * Shape systems that Fortune 500 pharma depends on * Competitive compensation with meaningful equity You'd be Quinn's scientific voice at the partner table. Quinn finds things human teams miss — you make the call and deliver to partners in days, not quarters. If you want to define how AI gets used in drug discovery, let's talk.