About the role

We are looking for a Forward Deployed Engineer with 3–8 years of experience to own end-to-end healthcare customer engagements at Protege — a fast-growing AI training data platform backed by a16z and CRV. You'll be the technical engine behind live deals, turning complex, ambiguous healthcare data challenges into delivered outcomes under tight deadlines.

What will you be doing?

Own customer engagements from scoping through delivery — translating vague requirements (e.g. 5M patient records across radiology, claims, genomics) into working data pipelines under tight timelines
Build custom data engineering solutions to handle massive volumes of structured and unstructured healthcare data (imaging, EHR, claims) at scale
Go on-site with customers and data partners to walk their engineering teams through working with data at scale
Identify gaps between Protege's platform and customer needs, then build the last-mile solutions that close those gaps
Surface repeatable patterns back to product to help convert bespoke implementations into scalable platform capabilities

Key Requirements

Strong data engineering background with experience building batch data pipelines at massive scale (structured and unstructured data)
Proven track record of high ownership — founding engineer, early-stage startup, or equivalent end-to-end technical accountability
Customer-facing experience: ability to scope ambiguous problems, communicate tradeoffs clearly, and guide technical stakeholders
Strong SQL and hands-on experience processing large volumes of data across diverse formats
Thrives in fast-paced, high-intensity environments — nights and weekends are not uncommon when deals are live

Protege is building the infrastructure layer for AI training data — solving what is arguably the biggest unmet need in AI today: getting access to the right data to train models.

The process of acquiring high-quality training data is time-intensive, incredibly expensive, and often ends in failure. Data holders don't know where to start and are rightly concerned about governance, intellectual property, and security implications. AI companies can spend years finding and negotiating access to the data they need.

Protege solves these problems by providing an easy-to-use platform that connects data holders with vetted data users — facilitating the secure, efficient, and privacy-centric exchange of AI training data.

What makes Protege unique is that they're not just a data broker. The company operates an internal Data Lab that transforms and enriches datasets specifically for AI training, making them significantly more valuable for frontier AI labs and model builders. They partner with organizations across healthcare, media, and other verticals to provide high-quality, real-world training data.

Key highlights:

$65M in total funding — $55M Series A (August 2025) backed by Andreessen Horowitz, CRV, Bloomberg Beta, Footwork, Shaper Capital, Liquid 2 Ventures, Flex Capital, and a $10M Seed round with investors including Adam D'Angelo and SV Angel.
Explosive growth — the company hit its entire 2026 goal by June, growing far faster than expected.
~80 employees and scaling rapidly with a lean, high-trust team of builders.
Operating at massive scale — processing up to a trillion images in a single delivery for customers.

Solving AI's data problem is a generational opportunity. The company that succeeds will be one of the largest in AI — and in tech. Protege is positioned to be that company.

Forward Deployed Engineer, Healthcare

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