AI & Data Systems Engineer (Remote)

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!

• →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 preserved and accessible.
• →Other responsibilities as assigned by your Manager/Supervisor

• 3 - 6 years of hands-on experience in a technical role spanning some combination of software development, data engineering, and infrastructure or DevOps work.
• Experience implementing and deploying AI solutions in a production environment, including model integration, API usage, and operational maintenance.
• Proficiency in at least one general-purpose programming language (e.g. Python) and comfort picking up new languages or frameworks as the work demands.
• Experience building and maintaining data pipelines, including working with APIs, relational databases, and cloud data platforms.
• Working knowledge of DevOps practices and tools: CI/CD pipelines, containerization (Docker), cloud infrastructure (AWS, GCP, or Azure), and infrastructure-as-code concepts.
• Demonstrated ability to read and understand existing codebases, including prototypes or AI-generated code, assess their quality, and refactor or rebuild them as appropriate.
• Familiarity with enterprise business systems such as Snowflake, Salesforce, NetSuite, or similar platforms, including working with their APIs and data models.
• Strong attention to detail.
• Good written and verbal communication skills; able to explain technical decisions clearly to non-technical colleagues.
• Bachelor’s degree in Computer Science, Engineering, or a related field preferred; equivalent practical experience accepted.