Product Engineer III - R37
Posted Oct 2, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a product you shipped end to end with React and Node or Python. What decisions did you own?
What they are checking: Whether they have real full-stack ownership, which this startup-style lab expects.
Tell me about a time you worked closely with design and research and changed your plan because of what they found.
What they are checking: Whether they collaborate and let evidence change their mind instead of building their own idea.
Tell me about a time you shipped something that did not work for users. What did you learn?
What they are checking: Whether they learn from real usage and iterate, which startup-level ownership requires.
Why does making denials preventable before a claim is submitted matter to you as an engineer?
What they are checking: Whether they understand the business problem well enough to make good product tradeoffs.
What would you do
You sit with a utilization management nurse and see they ignore half of what Phare OS suggests. What do you do next?
What they are checking: Whether they dig into real workflows to find why, rather than assuming users are wrong.
An AI model returns low-confidence results for some cases. How should the product present those?
What they are checking: Whether they think about uncertainty and human review instead of hiding it from users.
About the work
How would you design a UI that shows an AI's recommendation on a coding or authorization case so a reviewer trusts it but still checks it?
What they are checking: Whether they can make AI output clear and explainable, which is the product eye this role is hiring for.
How do you handle protected health information in a web app, from the API down to logs?
What they are checking: Whether they build with HIPAA-level care by default in a system holding patient encounters.
How would you build a frontend that stays fast when a reviewer is working through a long queue of cases?
What they are checking: Whether they think about performance in real user workflows like pagination, caching and optimistic updates.
How do you decide what to test in a full-stack feature that calls a machine learning service?
What they are checking: Whether they test contracts, edge cases and failure modes, not just the happy path.
How do you instrument a feature so you know whether it actually saves reviewers time?
What they are checking: Whether they measure outcomes, since the lab is judged on real impact at scale.
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Practice this out loudThe full job description
As published by R1 RCM.