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.