The Atria Health Institute is a membership-based primary and specialty health care practice with a focus on prevention and longevity. We bring together a multidisciplinary team of renowned physicians to provide proactive, preventive, and precision-based care for Atria members and their families. All care, including primary care, advanced screening and diagnostics, urgent care, specialty care, 24/7 home visits, and imaging is included in members’ annual fee. Our mission is to make healthspan and lifespan equal for all by translating science into medicine in real-time, all while bringing humanity back into health care. Delivering such robust, personalized, and preventive health care is complex and requires a team-wide dedication to excellence. We have state-of-the-art facilities with imaging and diagnostics on-site in New York City, Palm Beach, and Los Angeles, with home services hubs in Miami, Greenwich/Westchester, and the Hamptons in summer. Additional locations are forthcoming in the Bay Area, Miami, and in downtown Manhattan. At each of our Institutes, we deliver personalized preventive medicine grounded in up-to-the-minute science — all in serene environments designed for trust and comfort. About this role As a Senior AI Engineer at Atria, you will own the design, development, and production deployment of the LLM-powered systems that put AI directly in front of clinicians and members: agentic medical companions, clinical decision support, and the document understanding pipelines that turn unstructured clinical data into usable signal. You'll work end-to-end across orchestration, retrieval, evaluation, and deployment, building on a data estate that very few organizations in the world can match (whole-genome sequencing, advanced imaging, longitudinal labs and wearables, multi-generational family records) and translating clinical and operational requirements into AI products that are reliable, safe, observable, and (crucially) actually used. This is a hands-on, high-ownership role for an engineer who wants to do work with direct patient impact, and for whom "we'll add evals later" is the sort of phrase that ruins a perfectly good Tuesday. What you'll do • Architect and ship production AI systems end-to-end (orchestration, retrieval, inference, evaluation, and monitoring) for clinical decision support, document understanding, and agentic medical companions. • Design agentic workflows with custom skills and harnesses, using frontier LLMs and self-hosted models where appropriate, and resisting the urge to reach for an agent when a function call would do. • Lead development of structured information extraction pipelines from clinical documents, including automated verification systems and human-in-the-loop gates, where necessary. • Build robust evaluation infrastructure (golden datasets, LLM-as-judge, regression tests, online A/B evaluation) that gates every model and prompt change. Accuracy, safety, cost, and latency are first-class metrics. • Engineer for latency and cost: streaming, caching, prompt compression, model routing, and inference-cost budgets. • Own production health: trace- and span-level observability, prompt versioning, drift detection, cost monitoring, replay-from-production for debugging, PHI-safe logging throughout, and the general art of finding out something is broken before a clinician does. • Operationalize models trained by the AI Scientist team: serving, evaluation in production, rollback paths, and the feedback loops that turn real usage into training and eval data, rather than into a Slack thread nobody reads. • Partner with clinicians, the data engineering team, and our research collaborators to translate clinical requirements into specifications and deliverable systems. • Educate and mentor PMs and engineers across Atria on applied AI best practices, from prompt and evaluation design to choosing the right model for the job (which, surprisingly often, is not the largest one). Leadership and Influence • Set technical direction for AI engineering at Atria: architecture decisions, build-vs-buy calls, and the evaluation and observability standards the team actually works to. • Raise the bar on engineering practices, code review, observability, and incident response. Quietly, persistently, and with grace. • Drive vendor and partner technical due diligence for AI/ML vendors: BAA scope, PHI handling, sub-processor obligations, and IP terms. • Mentor other AI engineers through code review, design docs, and architecture decisions, with the patient conviction that good systems are made twice: once badly, then properly. Requirements • A bias to ship. You would rather have a rough thing in production tomorrow that you can measure and improve than a beautiful thing in a design doc next quarter. • A scrappy streak. You can pick up an unfamiliar tool, framework, or clinical concept on a Wednesday and have something working with it by Friday. • A serious drive to keep getting better. You read other people's code, papers, and post-mortems. You treat being wrong as cheap information rather than an event. • Hands-on experience building and operating LLM-powered or production ML systems: 4+ years. • Strong Python skills and deep familiarity with at least one production backend framework (FastAPI, Flask, etc.). • Hands-on experience with LLM orchestration, function/tool calling, and retrieval system design: chunking, hybrid search, reranking, and the rest of the unfashionable middle bit that makes RAG actually work. • Working knowledge of a cloud data warehouse (Snowflake strongly preferred) and modern data tooling such as dbt, Dagster, or Airflow. • A track record of designing evaluation systems for LLM applications — including the ones that caught regressions before users did, and the ones that didn't. • Familiarity with safety considerations for LLM applications: guardrails, adversarial testing, and the failure modes that tend to introduce themselves at 4pm on a Friday. • Practica