Machinify
via Greenhouse
Forward Deployed Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 11h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 181d ago
The date the source published, not the day we noticed it (2026-03-17). Last seen at its source 3h ago.
Is it remote?
Remote - US
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $160k–225k/yr
Middle 50% of 2159 listings that do state pay — Engineering · all levels · United States · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
greenhouse employer's own board first seen 18d ago · last seen 3h ago
The listing
Machinify is a leading healthcare intelligence company with expertise across the payment continuum, delivering unmatched value, transparency, and efficiency to health plan clients across the country. Deployed by over 85 health plans, including many of the top 20, and representing more than 270 million lives, Machinify brings together a fully configurable and content-rich, AI-powered platform along with best-in-class expertise. We’re constantly reimagining what’s possible in our industry, creating disruptively simple, powerfully clear ways to maximize financial outcomes and drive down healthcare costs.
We're hiring Forward Deployed Engineers (FDEs) to sit at the intersection of our product and our customers' hardest problems. You'll embed with customer teams, understand their workflows deeply, and build, configure, and deploy solutions on top of our platform that actually move the needle. This isn't traditional implementation work — you'll write production code, design data pipelines, and ship features that often make their way back into the core product.
What you'll do
You'll spend significant time on-site (or deeply async) with customer teams — engineers, analysts, operators, executives — to figure out what's actually broken and what's worth building. You'll prototype quickly, then harden what works: data integrations, internal tools, ML pipelines, custom UIs, automation. You'll own outcomes end-to-end, from the first whiteboard session to the version running in production six months later. You'll bring real signal back to our product and engineering teams, advocating for the platform changes that would have made your job easier. And you'll do all of this without a clean spec — figuring out the problem is half the work.
What we're looking for
Strong generalist engineering chops. You can move comfortably across the stack — backend services, data work, frontend when needed, infra when forced. We don't care which languages, but you should be the kind of person who picks up a new one in a weekend if the job calls for it.
Customer instinct. You can sit in a room with a non-technical user, ask the right questions, and translate what you hear into something buildable. You don't get defensive when feedback is messy. You can push back on a customer when their ask is wrong, and they'll thank you for it.
High agency and tolerance for ambiguity. You're allergic to waiting for permission. When the path forward isn't clear, you make a call and ship something. You're comfortable being the only engineer in a room of stakeholders.
Bias toward shipping. You'd rather have a working v0 in front of a user on Friday than a perfect design doc on Monday.
Nice to have: prior experience as a founder, early engineer at a startup, consultant at a top firm, or in roles where you owned a customer relationship technically. Background in data engineering, ML, or distributed systems. Willingness to travel — typically 25-50% depending on the engagement.