iollo has developed Quinn, our AI scientist to analyze multimodal biomedical data to discover new biology in hours, work that typically takes teams of scientists years. Quinn translates these discoveries into accelerated development of novel therapeutic strategies and interventions for extending healthspan.

Skills: Bioinformatics, Biotechnology, Drug development, Machine learning, Python, Genomics, Pharmacology, Molecular design, Genome engineering, Quantitative biology, Protein engineering, Prompt Engineering, Deep Learning, Data Analytics

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

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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.

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