Beacon recruits engineers who build and operate the pipelines that protect sensitive data, particularly in healthcare AI, where getting this wrong isn't just a bug, it's a compliance failure.
See how we source privacy engineersThese are real searches Beacon has run and closed, not hypothetical examples.
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A privacy engineer builds and operates systems that remove protected or personally identifiable information from data at scale, so it can be used safely for analysis, model training, or research. In healthcare specifically, this means working with structured, unstructured, imaging, and even waveform patient data, at volumes that can run into the tens of millions of records, while keeping compliance requirements built into the pipeline rather than bolted on afterward.
Direct de-identification experience is genuinely rare, most engineers have worked adjacent to sensitive data without ever owning the process of stripping it out safely. Our own search for this role started as targeted outbound: reaching specific candidates on LinkedIn directly, before a single job application came in.
Not just familiarity with the concept. Ask what specific technique they used and on what kind of data.
Healthcare data work means real compliance stakes. Look for candidates who've operated under that pressure before.
The engineering craft still has to be real, privacy expertise doesn't substitute for core technical skill.
Candidates open to exploring a new industry, like healthtech specifically, often make the strongest long-term hires in this space.
On Beacon's own recent search, a privacy engineer role at a healthcare AI company closed at $140K total comp, sourced through direct outbound rather than inbound applications.
This is a search that usually starts with outbound, not a posting. We identify candidates with real de-identification or adjacent sensitive-data experience directly, and we stay engaged through onboarding to confirm a strong start.
Builds and operates systems that remove protected or personally identifiable information from data at scale, particularly in healthcare and other regulated industries.
Direct de-identification experience is genuinely rare. Our own search started as targeted outbound on LinkedIn before any posting went live.
Our own recent search, at a healthcare AI company, closed at $140K total comp.
Direct hands-on experience working with de-identified data, real Python and SQL skill, and comfort operating in a regulated, high-stakes environment.
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