AI & Data Systems Engineer

Ceribell │ Point-of-Care EEG

AI & Data Systems Engineer responsible for implementing internal AI tools at Ceribell. Collaborating with stakeholders and managing infrastructure for data systems in healthcare technology.

Posted 6/5/2026full-timeRemote • California • 🇺🇸 United StatesMid-LevelSenior💰 $141,000 - $173,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformPython

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

Performance-based incentive compensation (varies by role)
Equity opportunities
100% Employer paid Health Benefits for Employees
50% - 70% Employer paid Health, Dental & Vision for dependents (depending on plan selection)
100% paid Life and Long-Term Disability Insurance
401(k) with a generous company match
Employee Stock Purchase Plan (ESPP) with a discount
Monthly cell phone stipend
Flexible paid time off
13 Paid Holidays + 3 Company Wellness Days
Excellent parental leave policy
Fantastic culture with tremendous career advancement opportunities
Joining a mission-minded organization!

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonAI solutionsdata pipelinesAPIsrelational databasescloud data platformsCI/CD pipelinescontainerizationinfrastructure-as-codecode refactoring

Soft Skills

attention to detailwritten communicationverbal communicationcollaborationstakeholder engagementproblem-solvingtechnical tradeoff assessmentdocumentationdata ownershipincident response