AI & Data Systems Engineer

About Ceribell

Ceribell is a medical technology company focused on transforming the diagnosis and management of patients with serious neurological conditions. The Ceribell System is a novel, point-of-care electroencephalography (“EEG”) platform specifically designed to address the unmet needs of patients in the acute care setting, and is being used in hundreds of community hospitals, large academic facilities and major IDN's across the country. Our entire team is driven by a shared commitment to transforming the landscape of critical care through our rapid seizure detection technology, come join the movement!

Position Overview :

The AI & Data Systems Engineer will be a key individual contributor on Ceribell ’s Data Architecture & Engineering team, responsible for much of the hands-on implementation work that turns architectural plans into reality. This role exists at the intersection of software development, data engineering, and DevOps as someone who is equally comfortable writing application code and managing the infrastructure those systems run on.

A significant part of this role involves taking ideas and early-stage prototypes for AI-powered internal tools and engineering them into stable, supportable internal applications. The ideal candidate is a versatile builder who can move fluidly across layers of the stack, understands when something is production-ready versus when it needs to be rebuilt properly, and takes pride in writing code and infrastructure that others can maintain and build on.

What you'll do:

• Implement internal tools and applications based on architectural direction from the Director of Data Architecture & Engineering, including taking stakeholder prototypes and re-engineering them into production-grade systems.
• Manage and maintain the infrastructure supporting internal AI tools and data systems, including deployment, configuration, monitoring, and incident response.
• Write clean, well-documented code across whatever languages and frameworks the work requires; apply sound engineering practices around testing, version control, and code review.
• Evaluate and integrate AI tools and third-party services into internal workflows where they provide clear value, with attention to security, cost, and long-term maintainability.
• Contribute to data quality practices: implementing automated checks, investigating pipeline failures, and helping establish clear data ownership and lineage.
• Collaborate with stakeholders across the business to understand requirements, surface technical tradeoffs, and deliver solutions that meet actual needs rather than assumed ones.
• Support access control and permissions management across systems and tooling, contributing to the team’s broader security and governance practices.
• Maintain thorough documentation of systems, data flows, and processes so that institutional knowledge is p