### **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.
Visa sponsorship is not available for this role.
