About Ivy League Staffing

Modern healthcare AI companies don't fail because they lack ideas. They fail because they underestimate what it takes to build regulated, production-grade medical products—and infrastructure is where most of them break.

The jump from demo to deployment is where companies die. Not because the model doesn't work, but because they can't maintain performance across clinical environments, can't build audit trails that satisfy post-market surveillance, or can't handle the operational reality of hospital IT at scale.The engineers who can build those systems in healthcare are exceptionally rare. Ivy League Staffing exists for this exact hiring problem.We place senior MLOps, ML Infrastructure, and Platform Engineering roles in healthcare imaging and regulated medical AI. Engineers who've shipped production ML systems under FDA and CE mark scrutiny, maintained performance across clinical environments, and owned real-world reliability at scale.Many firms pattern-match on keywords such as PyTorch, Kubernetes, and MLOps, and often miss the architectural gap between research ML and production ML in regulated healthcare. They struggle to evaluate whether a candidate has debugged model drift across scanner manufacturers, built audit-compliant deployment pipelines, or optimized inference for sub-three-second clinical workflows.We operate at the system level. Our searches begin with technical architecture conversations about your production constraints, regulatory requirements, and where current infrastructure breaks. We map clinical-grade engineering problems to specific human capabilities.Every search is intentional. When we present a candidate, we've already done deep technical and domain-specific diligence to assess their readiness for regulated production systems. The cost of an empty infrastructure role is roadmap paralysis, team burnout, and delayed regulatory submissions. The cost of a wrong hire is worse: technical debt in audit trails, architectural decisions that fail under clinical deployment, and systems that collapse under real-world data diversity.You need candidates who've maintained production ML systems through post-market surveillance. Who understand DICOM integration, HIPAA-compliant MLOps, and the difference between academic datasets and community hospital imaging data.Precision over volume. We work with a select number of clients where technical precision matters more than speed to hire.The outcome we work toward is simple. The role is filled correctly, your infrastructure roadmap has an owner, and you're building product again.