Why Healthcare AI Companies Struggle to Find ML Infrastructure Engineers

By Michael Johns, Founder of Ivy League Staffing

You've been searching for six months.You've interviewed candidates with exceptional credentials. Experience at leading tech companies. Strong backgrounds in PyTorch, Kubernetes, distributed systems. They've built impressive ML infrastructure at scale. On paper, they look great.But when you ask about maintaining model performance across different scanner manufacturers, the answer is vague. When you dig into their experience with audit-compliant deployment pipelines, they pivot to talking about CI/CD in general. When you ask how they've handled post-market surveillance for a regulated ML system, you realize they've never actually done it.The interview ends politely. You move on to the next candidate. The role stays open.This isn't a talent shortage. The problem is much more specific. You're searching for production ML engineers in healthcare, but the vast majority of ML engineers have only ever built research systems.The Invisible Gap
Most ML engineers, even very good ones, work in environments where:Models are evaluated on static benchmarks, not live clinical data"Deployment" means pushing to a cloud service, not integrating with hospital PACS systemsPerformance degradation is a metric to monitor, not a regulatory event requiring root cause analysisSystem failures are operational incidents, not adverse events reported to the FDAThese engineers haven't debugged why a model trained on academic datasets fails when it encounters community hospital imaging protocols. They haven't built MLOps pipelines where every model version, every training run, every deployment must be traceable for audit. They haven't maintained a production system through post-market surveillance where "the model drifted" isn't an acceptable explanation.They've built impressive ML systems. They've just never built medical devices.What the Role Actually Requires
The engineer you need has lived through specific, unglamorous failures:Scanner heterogeneity. They've diagnosed why model performance collapsed when deployed to Siemens scanners after training on GE data. They understand that domain adaptation in healthcare isn't a research problem. It's an operational requirement.Audit trails. They've built deployment systems where you can answer, six months later, exactly which model version produced a specific clinical output, what data it was trained on, and what validation it passed. Not because it's interesting architecture, but because regulators will ask.Inference constraints. They've optimized models to run in under three seconds because radiologists won't wait longer, and they've done it without sacrificing the performance metrics your FDA submission depends on.Post-market surveillance. They've maintained a production ML system after it shipped, monitoring for drift, updating models under regulatory constraints, and managing the reality that "just retrain it" isn't an option when you need documentation and, in some cases, a new submission.Operational ownership. They understand that in healthcare AI, the ML engineer doesn't hand off to ops. They are ops. When the system fails at 2am in a hospital, they own the recovery, technically and regulatorily.These experiences don't come from ML research roles. They don't come from big tech. They come from having shipped and maintained regulated ML products in clinical environments.Why This Gap Exists
The pathway that produces great research ML engineers doesn't produce regulated production ML engineers.Academic ML programs optimize for novel methods and benchmark performance. Industry ML roles at tech companies optimize for scale and iteration speed. Neither optimizes for auditability, regulatory compliance, or clinical integration.The number of engineers who have actually maintained a production ML system under FDA oversight, in a real hospital environment, with real operational constraints, is vanishingly small. And most of them aren't actively looking for jobs.Standard recruiting approaches fail because they pattern-match on credentials and keywords that signal research ML competence, not production ML competence in regulated environments. The resume looks identical. The interview reveals the gap, but only if you know what questions to ask.The Real Problem
You're not searching for a needle in a haystack. You're searching for a specific type of needle that most recruiting firms don't know how to identify, in a haystack where most needles look almost identical but can't do the job you need.Meanwhile, your roadmap is blocked. Your team is burning out from failed interviews. Your regulatory timeline is slipping.The role stays open because the gap between what you need and what most recruiting firms can deliver is architectural, not just procedural.This is the gap Ivy League Staffing was created to close.