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 AI & Data Systems Engineer will be responsible for implementing internal tools and applications, managing infrastructure for AI tools, and ensuring data quality practices across the organization.

Responsibilities:

• 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

Requirements:

• 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