Staff / Principal Software Engineer

Team: Engineering

Location: Palo Alto, CA

Commitment: Full Time

Workplace Type: onsite

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.

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.

Preferred Qualifications:

• Domain knowledge or prior experience working with computational biology, genomics, transcriptomics, protein/molecular structures, healthcare, or scientific software.

• Familiarity with building tools for scientific simulation, automated laboratory experiment management, or complex workflow orchestration.

• Experience building unified computational frameworks (similar to our AIDO Foundry) that accelerate the development and deployment of AI-native platforms.