Founding Forward Deployed Scientist - Agentic Pharma R&D

About Quinn

The AI that delivers pharma R&D decisions in days, not months

Tech description:

Our tech stack includes:
Infrastructure: AWS (Lambda, ECS, Fargate, S3, App Runner, Cognito, Aurora PostgreSQL, SQS), deployed via AWS CDK/Terraform
Backend: Node.js, Nest.js, R, Python (for AI/ML services)
Frontend: React, TypeScript, Tailwind, deployed on Vercel
Databases: PostgreSQL, MongoDB, Redis
Architecture: Event-driven microservices, serverless functions

Job description:

### **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.

Skills:

Python, Machine Learning, Bioinformatics, Prompt Engineering, LLMs, Evals, AI Agents