Lead, AI

We are looking for an Applied AI Engineer to build, evaluate, and continuously improve clinical AI agents and supervised ML Models. You will work at the intersection of software engineering, LLM systems, evaluation, model improvement, and deep healthcare workflow understanding. Your job is to turn frontier model capability into reliable production behavior: agents that read complex medical records, use the right clinical and coding context, call the right tools, produce auditable outputs, and improve from real-world failures. You will be embedded in hard healthcare problems, clinical documentation integrity, medical coding, denial prevention, appeals, revenue cycle workflows, and payer logic — and will own the loop from problem framing to agent design, evaluation, deployment, trace analysis, and ongoing improvement. The ideal candidate is a strong engineer who thinks like an applied scientist: rigorous about measurement, comfortable with ambiguity, excited by messy real-world data, and motivated by closing the gap between impressive demos and dependable production systems. Most AI roles are either too research-heavy or too product-light. This role sits in the middle. You will not only write prompts or run experiments. You will own whether an agent actually works in production. That means understanding the workflow, designing the system, building the evals, inspecting failures, improving the agent, and proving that the improvement matters. Responsibilities Design, build, and continuously improve production AI systems for complex healthcare workflows, including documentation, coding, denial management, appeals, and revenue cycle automation. Prototype new AI capabilities from 0 to 1, then harden them into reliable, explainable, auditable production systems with clear contracts, monitoring, evidence, and performance gates. Build rigorous evaluation and feedback loops using expert review, production logs, model outputs, and benchmarks to measure performance, regressions, reliability, cost, and business impact. Own outcomes end to end: understand the workflow, design the system, inspect failures, improve the system, and prove that the improvement matters. Set technical direction for a portfolio of projects, review designs, and mentor engineers as a technical lead. Partner with research, ML engineering, platform, and product teams on model selection, training, infrastructure, and Productionalization. Educational Qualifications Bachelor's degree in computer science, Engineering, Mathematics, or a related quantitative field, or equivalent practical experience. Master's or PhD in Computer Science, AI/ML, or a related field is a plus. Industry Experience 5+ years of Applied Research, ML engineering, research engineering, software engineering, or applied AI experience. A track record of shipping and operating production systems, ideally in ambiguous, high-stakes domains. Prior experience in healthcare, revenue cycle management, or other regulated industries is a plus, not a requirement. Most recent employment from Top Tier preferred companies like