FoundHuman

Insights

Notes from a decade of recruiting
for digital health.

Long-form writing on hiring strategy, search design, and the operator side of talent in AI-powered healthcare: method, economics, and guides to the hardest roles.

Featured · Field notes7 min read

The dual-fluency problem.

The talent bottleneck in AI-powered healthcare is the rare person who is fluent in engineering and clinical work at the same time, and that person is hard to find.

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Benchmark6 min

What fifty-eight placements taught us about time-to-hire.

Across 58 documented placements from 2023 to 2025, the median search ran 43 days from kickoff to accepted offer. The pattern underneath the number is the useful part.

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Method6 min

Why blind scoring outperforms the loudest voice in the debrief.

Unstructured panel debriefs reward confidence and seniority and mistake both for signal. A weighted rubric scored blind changes who the room agrees with.

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Economics7 min

The math founders should run before signing a contingency agreement.

Contingency fees look like they cost nothing until a hire lands. Run the per-role math, and the incentives, before you sign.

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Timing7 min

The post-raise hiring window.

The months right after a round are when the hiring plan succeeds or quietly dies. Treat the window with the same rigor you brought to the raise.

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Timing6 min

What a role open forty-five days is actually costing you.

A req open past forty-five days is usually stuck on a decision no one has made, not starved for candidates. Audit that before you add more sourcing.

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Economics6 min

Retention at twelve months is the only hiring metric that compounds.

Time-to-fill measures speed. Retention measures quality, and quality is the only part that compounds. Ask any partner for their number.

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Method7 min

What a twenty-two point playbook contains.

Before a single candidate is sourced, the search is written down. What goes into that document, and why writing it changes the outcome.

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Role guide8 min

Hiring your first VP of Engineering for clinical-grade AI.

The first engineering leader for a clinical-grade AI product is rarely your strongest coder promoted. You want someone who has shipped models into a regulated environment and can explain the audit trail.

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Role guide8 min

The first go-to-market hire who can actually sell into healthcare.

Selling AI into health systems is a long-cycle, multi-stakeholder, evidence-driven sale. Your first commercial hire has to have survived one, not just sold software.

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Role guide8 min

Health AI agents need safety and evaluation engineers.

As agentic systems move into care navigation and documentation, a new role is forming: the engineer who owns the question of whether the thing is safe to ship.

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Role guide8 min

RCM automation runs on payer-data fluency.

AI applied to the revenue cycle lives or dies on people who understand how payer data actually behaves: the edge cases, the denial codes, the clearinghouse quirks.

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The conversation begins
upstream of the role.

The first calls that matter are strategic. They surface what the business needs to accomplish over the next four quarters and where the talent question actually sits. Fifteen minutes is enough to start. We come prepared.

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