About Quinn

The AI scientist for high-stakes decisions in drug development

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 is a Stanford-founded startup. We've built Quinn, an AI scientist that finds discoveries human teams miss. We're looking for a founding computational scientist to expand Quinn's proven capabilities and implement it with biotech/pharma companies. Quinn already works with Fortune 500 pharma partners, reasoning through complex data to generate hypotheses, interpret biology, and deliver strategic recommendations. Think of Quinn as a brilliant colleague who needs a scientific partner to expand into new therapeutic areas and create a rapid feedback loop between real-world needs and platform development. ### **What makes this role unique** > * Direct impact on drug discovery - partners use your work to make critical program decisions > * Own the entire partner journey from initial scoping to successful deployment > * Build the playbook for implementing AI in drug discovery across therapeutic areas > * Bridge the gap between cutting-edge AI capabilities and real-world pharma needs > * Shape product direction based on hands-on partner experience ### **Responsibilities (50/50 split between Quinn expansion and partner success)** **Quinn Expansion:** > * Extend Quinn to new therapeutic areas and use cases > * Work with engineering to scale capabilities to new domains > * Develop best practices for different discovery challenges > * Maintain scientific rigor as Quinn tackles increasing complexity **Partner Success:** > * Implement Quinn for each partner's specific needs > * Guide partners to maximize impact of Quinn's discoveries > * Validate biological relevance before partner delivery > * Present findings and recommendations to executive teams > * Gather feedback to identify future expansion opportunities ### **What you'll need** > * PhD in Computational Biology, Bioinformatics, or related field (or equivalent industry experience) > * 3+ years applying computational methods to drug discovery challenges > * Deep understanding of the drug discovery pipeline from target ID through clinical trials > * Experience validating scientific discoveries and ensuring biological relevance > * Strong scientific judgment to assess AI-generated discoveries and recommendations > * Programming skills to expand Quinn's capabilities (Python preferred) > * Track record of translating complex scientific findings into actionable insights > * Excellent communication skills - presenting AI discoveries to diverse audiences > * Ability to bridge AI discovery capabilities with real-world drug development needs > * Comfort working with AI as a scientific colleague ### **Nice to have** > * Experience at pharma/biotech companies or CROs > * Knowledge of specific therapeutic areas (oncology, neuroscience, immunology) > * Publications in computational drug discovery > * Experience with LLMs and modern AI architectures > * Consulting or customer-facing experience > * Understanding of regulatory requirements (FDA, EMA) ### **Why join us** > * Work alongside an AI scientist that performs complete scientific discovery beyond human scale > * Be the founding computational lead expanding AI's reach in drug discovery > * Guide how the world's most advanced pharma companies leverage AI for discovery > * See direct impact - partners make critical decisions based on Quinn's discoveries > * Join at the perfect stage: proven product with massive growth ahead > * Collaborate with Stanford scientists and Y Combinator network > * Competitive compensation with meaningful equity > * Shape how AI scientists and human experts collaborate to transform drug discovery ### \--- If you're excited about AI-driven drug discovery but your background doesn't perfectly match, we still want to hear from you. We value diverse scientific perspectives and are looking for passionate scientists who want to transform how drugs are discovered.

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