Lead Platform Engineer — AI Biology Systems

GenBio.ai, Inc.

Palo Alto, Northern (CA, KY)

Hybrid

USD 250,000 - 350,000

Full time

14 days+

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Job summary

GenBio AI, Inc. in Palo Alto is seeking an experienced Staff to Principal-level Software Engineer to build the user-facing platform that enables computational biologists, experimental scientists, and pharmaceutical researchers to interact with a growing Virtual Cell world.

This senior IC will shape architecture and drive end-to-end platform development. You will partner with AI researchers, product teams, and biologists to translate foundational models into reliable, accessible scientific

Qualifications

Extensive industry experience (typically 8+ years) operating at a Staff or Principal level, with a proven track record of building production web services, scalable APIs, and complex distributed backend systems.
Exceptional software engineering fundamentals, demonstrating a mastery of clean architecture, modular system design, and the ability to write highly maintainable, performant code.
Deep, hands-on experience integrating large-scale AI, LLMs, or machine learning inference systems into highly available production environments.
Proven ability to solve challenging, ambiguous systems problems from the ground up, moving from concept to scalable execution.
Outstanding cross-functional communication and leadership skills, with the ability to bridge the gap between deeply technical engineering teams, AI researchers, and scientific product teams.

Responsibilities

Act as a senior technical leader to build the software platform for programmable biology, developing production-grade backend services and APIs for our Virtual Cell products.
Design clean, robust interfaces that seamlessly connect frontend applications, core business logic, massive biological databases, and state-of-the-art AI inference engines.
Architect and develop highly scalable systems for complex biological simulations, experiment management, workflow orchestration, and the processing of multimodal biological data.
Drive continuous improvements in platform performance and reliability through highly efficient architecture design, strategic caching, asynchronous execution, and optimized distributed infrastructure.
Collaborate closely with AI researchers to translate cutting-edge foundation models into reliable, user-facing scientific products that enable virtual perturbations and hypothesis generation.
Work cross-functionally across frontend, backend, infrastructure, and machine learning teams to deliver deeply intuitive computational experiences for biologists, experimental scientists, and pharmaceutical researchers.