
Alnu Health is recruiting a full-stack AI engineer in New York to develop clinical AI systems that integrate patient memory, knowledge retrieval, and model orchestration into a healthcare platform.
The role emphasizes engineering trustworthy clinical AI through evaluation frameworks, deterministic constraints, and observability rather than maximizing model scale—reflecting a philosophy that AI should strengthen rather than replace the clinician-patient relationship.
What happened
Alnu Health, a healthcare platform, is hiring a Full-Stack AI Engineer for Clinical Systems based in New York to build the intelligence layer of its platform, working on systems that transform clinical knowledge and patient context into personalized support.
Why it matters
The role reflects a shift in healthcare AI away from simply wrapping chat interfaces around large language models; instead, Alnu emphasizes engineered systems with clinical validation, longitudinal patient memory, deterministic constraints, and clear observability—positioning AI as a tool to extend clinicians rather than replace them.
What to watch
The position requires 3 to 5 years of professional software engineering experience, deep proficiency in Python and TypeScript, hands-on experience with production LLM systems beyond basic API integration, and understanding of RAG, embeddings, and model orchestration; prior healthcare, clinical data, or regulated-environment experience is particularly valued.
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Alnu Health's job posting signals a deliberate departure from the prevailing approach to healthcare AI. Rather than adopting the largest available language model and wrapping a chat interface around it, the company articulates a systems engineering philosophy centered on clinical reliability, patient safety, and clinician augmentation. The posting emphasizes continuous model evaluation and architectural flexibility—the ability to assess, adopt, or replace models and strategies as better options emerge—rather than building organizational lock-in around any single model, provider, or framework.
The technical requirements reflect this philosophy: the ideal candidate has not just deployed LLM APIs but has engineered production systems incorporating RAG (retrieval-augmented generation), embeddings, structured retrieval, tool orchestration, and evaluation frameworks for nondeterministic software. Equally important, the candidate must understand the distinction between semantic proximity in vector similarity and clinical relevance in patient context, and must be able to design feedback loops that turn clinical expert review into measurable system improvements. The emphasis on longitudinal patient memory, deterministic constraints, and observability suggests Alnu is solving problems of consistency and auditability that generic chat interfaces cannot address.
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