- Salary
- $170k – $230k/yr
- Location
- San Francisco, California, US
- Workplace
- Remote
- Type
- Full-time
- Department
- Education
- Education
- PhD
- Source
- Y Combinator
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.
