A compliance-ready API? layer that helps healthcare software teams validate, monitor, and safely deploy clinical AI workflows inside EHR, virtual care, and health system products.
Added Jun 2, 2026
Last signal 2w ago
Healthcare companies are rapidly embedding AI into clinical documentation, diagnostics, care delivery, and research workflows, but clinical-grade deployment requires safety, validation, monitoring, and integration with existing systems. Builders serving clinicians and patients need infrastructure that works with real clinical data and supports high-stakes workflows without behaving like generic AI tooling.
The product would provide a hosted validation and monitoring layer for clinical AI applications, including workflow-specific evaluation, safety checks, audit trails, model performance tracking, and integration hooks for EHR and virtual care platforms. It would help teams test AI outputs against clinical standards before deployment and continuously monitor real-world usage for quality, drift, and risk signals.
Multiple healthcare AI companies are hiring around clinical-grade models, AI care, diagnostic AI, and clinician copilots, suggesting rapid production adoption. As AI moves from experimentation into patient-facing and clinician-facing workflows, deployment infrastructure becomes a buying need rather than a research task.
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Your mission will be to ensure our AI Health Companion behaves safely, reliably, and helpfully — for millions of patients and practitioners. You will work in a cross-functional team building robust evaluation pipelines for agentic AI systems, contributing directly to improving the quality and safety of AI-powered healthcare.
Evaluate and guide the responsible use of AI and agentic frameworks in clinical product contexts, with appropriate attention to safety, reliability, and architecture discipline Partner with clinical operations to ensure product decisions reflect how clinicians actually work
Set the standard for AI-native workflows that support real-time, conversational, and long-running interactions across diverse healthcare contexts. Own the safety and evaluation bar across model evaluation, safety testing, and observability. Define the gates every agent must clear before production.
Develop and refine AI-native workflows that support real-time, conversational, and long-running interactions across diverse healthcare contexts. Drive continuous improvement in model evaluation, safety testing, and observability , ensuring every agent interaction meets clinical safety standards
Drive continuous improvement in model evaluation, safety testing, and observability , ensuring every agent interaction meets clinical safety standards Proven track record building and shipping AI- or ML-powered products in production environments.
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