Staff Applied AI Engineer, Product & Agent Performance

Arcadia is the most trusted healthcare platform powering outcomes. We transform complex healthcare data into trusted intelligence, helping providers, payers, and life sciences organizations act with clarity, make confident decisions, and achieve measurable clinical, operational, and financial outcomes. 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 to AI and automation is governed and transparent — designed to strengthen human expertise, not replace judgment or obscure responsibility. Hundreds of organizations rely on Arcadia to improve cost, quality, and outcomes. Backed by Nordic Capital, we continue to invest in our platform, AI capabilities, and people as we pursue our purpose: helping healthcare deliver better outcomes for every person, every community, and every generation. Why This Role Is Important to Arcadia 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. This role owns how our agentic capabilities perform at that same scale: accurate, transparent about their own confidence, and safe for the clinicians, care teams, and patients who depend on them. As a staff-level individual contributor, you will own the product-layer decisions that shape agent behavior, including prompting, retrieval and context, memory and state, evaluation, and escalation, while partnering with Product and Engineering on the systems that support them. Your work will help 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 have established a production-grounded baseline for priority agentic workflows, with documented failure modes, severity-weighted evaluation rubrics, and a clear measurement plan You have mapped the current retrieval, context, memory, and escalation patterns and identified the highest-value opportunities to improve reliability, calibration, and cost You have earned trust across Product and Engineering by turning production evidence into clear, actionable recommendations In 6 months Production-representative evaluation suites and regression checks inform model-change decisions for priority agentic workflows You have delivered measurable improvements in accuracy, reliability, steerability, latency, or cost for one or more priority workflows Human-review and escalation behavior has been validated under adversarial and edge-case conditions, with decision criteria and ownership boundaries clearly documented In 12 months Arcadia has a repeatable product-layer AI performance practice that moves from production failure to diagnosis, experiment, evaluation, and release decision High-severity regressions are caught earlier, and agent behavior is more transparent, calibrated, and trustworthy at scale Model cards, intended-use guidance, limitations, and performance documentation are current and useful to product and customer-facing teams What You'll Be Doing Design and iterate on agent behavior across real, live workflows, including long- horizon , multi-turn agentic tasks Design retrieval and context architecture so the right source data reaches a model in the right structure and agents remain grounded in real data rather than filling gaps with assumptions Design memory and state handling across multi-turn and multi-agent flows, determining what is carried forward, summarized, or dropped and why Create context and prompt templates that combine few-shot examples, structured formatting, and reasoning scaffolding for consistent agent behavior Improve performance through prompting, tool-use strategy, and context construction, validated through direct experimentation rather than guesswork Build and run evaluations