Leads engineering execution for AI-driven computational biology systems, translating research into scalable production platforms. Responsibilities include setting roadmaps and delivery timelines, contributing code, architecting AI infrastructure, operationalizing foundation models, enforcing testing and CI/CD standards, mentoring engineering teams, and coordinating with executives and research leaders. The role requires deep expertise in generative AI infrastructure, GPU clusters, distributed systems, and production deployment, with computational biology and startup experience preferred.

Build the user-facing software platform for a programmable Virtual Cell, including scalable backend services, APIs, distributed systems, biological simulations, experiment workflows, and multimodal data processing. Integrate AI and machine learning inference into reliable scientific products, improve platform performance and reliability, and collaborate with engineering, research, and scientific product teams.

Develop and improve ML models for structural biology, lead computational discovery projects with external partners, translate research into scientific impact, design and execute protein engineering/discovery campaigns, collaborate with experimental scientists, and apply software engineering best practices.