AI/ML Engineering Co-op bei Zushealth

AI at Zus allows personalized, coordinated healthcare driven by information that is comprehensive, accessible, and timely. Zus is NOT an AI company but many of its product features are AI driven.What we’re looking for…We are looking for an eager software engineering co-op to help build the next generation of healthtech tools. This co-op will be part of the AI group.  This group has members embedded in the product teams and also works independently on proof-of-concept projects. The ideal candidate will be able to contribute in either situation.In your role as an AI/ML Engineer Co-op, you will, under the guidance of senior AI group members, be responsible for conducting research to explore new methodologies and techniques, and integrating them into our product offerings.   You will develop prototypes to test and improve upon your innovations and develop feedback mechanisms to improve models with human oversight.  You will work with software engineers to help deliver CI/CD pipelines, and automate workflows to ensure reliable and scalable model operations. You will be responsible for presenting your learnings and helping the team leverage these methods and techniques.



As part of our team, you will

Algorithm & Experimentation: Formulate hypotheses, build rapid prototypes, and run experiments

Evaluation & Diagnostics: Design task-specific metrics and effective methods for their estimation

Collaboration: Work cross-functionally with engineers, product managers, and other AI group members 

End-to-End Ownership: Take prototypes to production, collaborating with engineers to deploy, and continually monitor and evaluate models

You're a good fit because you have

Hands-on experience with LLMs and classical ML methods (must-have)

Hands-on experience with agentic frameworks and tools such as LangGraph, Strands, LangFuse, MCP, etc.

Proficiency in Python (must-have)

Hands-on experience designing offline or online experiments: crafting task-specific metrics, computing bootstrapped confidence intervals, and conducting slice-based error analysis

Familiarity with cloud services (e.g., AWS, GCP, Azure)

Demonstrated curiosity—comfortable jumping into unfamiliar domains, papers, or codebases and learning fast

Strong verbal/written skills—able to explain complex ML concepts to diverse stakeholders

Demonstrated ability to work effectively in a collaborative team environment

It's a bonus if you have

Hands-on experience with LLMs or classical ML methods in production environments

Experience partnering with software engineers to ship, monitor, and iterate on AI/ML systems in production

Healthcare domain experience (claims, EHR, clinical NLP)

Bachelor's degree in Computer Science

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