Senior Manager, Applied AI Engineering
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About the Role
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 Senior Manager, Applied AI Engineering is a senior individual contributor role with broad ownership across Ceribell's internal AI engineering portfolio. This person will carry projects from initial problem definition through production deployment, operating effectively under conditions of ambiguity and incomplete specification. The technical bar is high — this is first and foremost an engineering role — but the ability to engage at the business layer and translate between technical and commercial realities is equally non-negotiable.
This role reports directly to the Director of Strategic Programs and Applied AI and works in close, ongoing collaboration with Engineering leadership. The successful candidate will direct external engineering contractors, partner cross-functionally with stakeholders across commercial, clinical, finance, and operations, and take on expanding ownership of the AI project portfolio as the function scales.
What you'll do:
- Assume end-to-end ownership of AI engineering projects across the portfolio — from requirements definition through architecture, development oversight, and production deployment
- Design GCP infrastructure across all environments, in partnership with engineering: Cloud Run, IAM, VPC network isolation, CI/CD pipelines, secrets management, observability, and security controls
- Translate business requirements into technically precise specifications — and proactively challenge scope or approach when a stronger path exists
- Lead company-wide AI infrastructure rollout and upskilling initiatives, including training programs, office hours, super-user community development, and AI governance scaffolding
- Develop PRDs, architecture documents, and project scopes that enable contractors and engineering partners to execute independently
- Forecast build timelines and resource requirements across a portfolio of concurrent initiatives
- Direct and quality-review the output of external engineering contractors
- Identify and advance system-level improvements proactively — with a recommendation and a plan, not merely an observation
- Build documentation and operational foundations designed to scale beyond any individual project
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