Senior AI Engineer

The the company the company the company is a membership-based primary and specialty the company 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 the company 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 the company care. Delivering such robust, personalized, and preventive the company 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 the company, 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 the company: 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 the company collaborators to translate clinical requirements into specifications and deliverable systems.
• Educate and mentor PMs and engineers across the company 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 the company: 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.
• Practical understanding of HIPAA, BAAs, PHI handling, and the everyday realities of working in a regulated environment without losing the will to live.
• Strong written communication: design docs, technical RFCs, and documentation that someone else can actually follow six months later.

Nice to have

• Experience in healthcare, clinical informatics, or other regulated domains (finance, biotech, anywhere people will sue you for being sloppy).
• Familiarity with medical ontologies (LOINC, SNOMED CT, ICD-10, RxNorm) and clinical data standards (FHIR, HL7).
• Experience with self-hosted model serving (vLLM, Triton, TGI, SGLang) and inference optimization. • Multimodal experience: vision, structured data, or time-series data alongside text.

Salary range: $190,000 $250,000 + performance-based bonus

Benefits

At the company, we are proud to offer every member of the the company team:

• Excellent the company and wellness benefits, fully covered by the company, effective date of hire

• OneMedical membership for employees & dependents, giving access to 24/7 virtual care

• Fertility & family planning

• Company-covered preventive the company screenings through partner hospitals (calcium score)

• Fitness Perks, including Wellhub +

• 401k contributions and 4% match starting after 6 months

• Flexible Time Off

• Continuing medical education (CME) and CEU support for professional licensure