Staff Applied AI Engineer, Product & Agent Performance
Arcadia · Remote (USA)
About this role
## Staff Applied AI Engineer, Product & Agent Performance ### About Arcadia Arcadia is a trusted healthcare platform powering outcomes. We transform complex healthcare data into trusted intelligence—helping providers, payers, and life sciences organizations act with clarity and achieve measurable clinical, operational, and financial results. Built on a comprehensive data foundation spanning tens of millions of patient lives, Arcadia combines advanced analytics and responsible AI to surface meaningful insights, coordinate action, and improve performance at scale. Our approach is governed and transparent—designed to strengthen human expertise, not replace judgment or obscure responsibility. ### Why This Role Is Important Arcadia’s data and analytics platform is used by hundreds of health systems, ACOs, payers, and life sciences organizations—touching tens of millions of patient lives. In this role, you will own how our **agentic capabilities** perform at that same scale: ensuring they are **accurate**, **transparent about their own confidence**, and **safe** for clinicians, care teams, and patients. As a staff-level individual contributor, you will make product-layer decisions that shape agent behavior, including: - Prompting - Retrieval and context - Memory and state - Evaluation - Escalation You will partner with Product and Engineering on the systems that support these capabilities, helping Arcadia make evidence-based launch decisions and scale responsible AI that is steerable, trustworthy, and ready for real healthcare workflows. ### What Success Looks Like **In 3 months, you will:** - Establish a production-grounded baseline for priority agentic workflows, including documented failure modes, severity-weighted evaluation rubrics, and a clear measurement plan - Map current retrieval, context, memory, and escalation patterns, identifying the highest-value opportunities to improve reliability, calibration, and cost - Earn trust across Product and Engineering by turning production evidence into clear, actionable recommendations **In 6 months:** - (Details not provided in the source text.)
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