Of all the postings in this thread, Kaiser Permanente's Medical Informatics listing is the most strategically revealing. Most companies in the thread are startups hawking the usual React/Node/Postgres stack. KP is a healthcare giant openly recruiting Machine Learning Engineers and Scientists for a Data Science Team alongside Backend Engineers for an Applications Team — and they're doing it on Hacker News, not on a medical journal job board.

The tech reveal (by omission): The posting deliberately doesn't name a stack. That's telling. In healthcare ML, the interesting infrastructure isn't the language — it's the data access. KP buries the lede in one phrase: "complete access to KP's massive EHR and a broad mandate to develop machine learning models." That sentence is the entire pitch. For an ML engineer, raw access to a longitudinal electronic health record covering ~12 million members is more valuable than any framework choice.

What it reveals about stage and direction:

• The split between a Data Science Team and an Applications Team signals a maturing org — they've moved past notebook-prototyping and are now productionizing models into clinical workflows.
• Mentioning "clinical decision support, auditing, and virtual care" in one breath shows three distinct product lines. Virtual care, in particular, signals that COVID accelerated their telehealth roadmap permanently.
• "ONSITE after the virus subsides" — a candid pandemic-era tell. KP wasn't built remote-first and isn't pretending otherwise.

Skills and trends highlighted: The dual hiring of ML scientists and backend application engineers reflects the industry-wide shift from "build a model" to "deploy a model into a regulated clinical environment." That second part — FHIR pipelines, audit trails, HIPAA-compliant serving infrastructure — is where most healthcare ML projects die. KP is staffing for the death zone.

Green flags: Real data, real deployment scale ("nationwide network"), real clinical impact. For an ML engineer tired of optimizing ad CTR, this is the rare posting offering models that affect patient outcomes.

Red flags: San Diego onsite-only narrows the talent pool considerably given remote norms in 2026. The posting is also vague on team size, reporting structure, and whether models actually ship — "broad mandate" can mean "we have political cover" or "nothing is prioritized." And working inside KP means navigating a 300,000-person bureaucracy; velocity will not resemble a startup.