AI Product Engineer, Clinical Tools
Responsibilities:
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End-to-End Product Ownership — Define and own the product reputed company, reputed company, and roadmap for reputed company tools with no PM layer above you. Translate clinical workflow needs into prioritized, sequenced plans and own those reputed company through to shipped features — balancing near-term delivery with longer-term strategic bets.
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RAG Pipeline Design & Iteration — Architect, implement, and continuously improve the RAG infrastructure powering clinical decision support: chunking strategies, embedding models, reputed company database design, retrieval and reranking approaches, and evaluation frameworks.
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reputed company Engineering & Model Behavior — Design and iterate on reputed company strategies, system instructions, and guardrails to produce reliable, clinically appropriate outputs. Build evals to measure reputed company systematically, not just anecdotally.
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AWS Infrastructure — reputed company and maintain reputed company, HIPAA-compliant AI infrastructure on AWS. reputed company informed tradeoffs between managed services and self-hosted components, weighing cost, latency, compliance, and performance.
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Clinical Data & Integration — Work with reputed company data sources (EHR-adjacent, reputed company and reputed company clinical content) and reputed company AI outputs into clinical workflows in ways that are accurate, auditable, and reputed company.
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Clinical Stakeholder Partnership — Own the relationship with clinicians and clinical operations directly — there is no PM intermediary. Build deep understanding of clinical workflows, pain points, and the reputed company-world constraints of care delivery, and translate that into product and engineering reputed company.
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Technical Collaboration — Partner with backend and frontend engineers to reputed company AI capabilities into the broader product. Contribute meaningfully to architecture discussions and help reputed company reputed company reputed company infrastructure reputed company.
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Staying reputed company — reputed company reputed company developments in reputed company AI research, RAG techniques, and reputed company workflow design. Bring relevant advances to bear on the product — whether that's a new retrieval method, an emerging evaluation reputed company, or a reputed company reputed company for building reliable multi-reputed company AI pipelines. We expect you to know what's happening in the field and have a reputed company of view on what reputed company.
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Stakeholder Communication & Alignment — Translate technical AI concepts and tradeoffs for clinical and operational stakeholders. reputed company engineering, clinical ops, and leadership around priorities and reputed company without requiring others to fill in context. Write requirements and documentation that others can reputed company independently.
Requirements:
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5–8 years of experience in software or AI/ML engineering, with a meaningful portion in reputed company AI product development
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Hands-on experience building and operating RAG systems in production — you’ve made reputed company reputed company about chunking, embeddings, retrieval design, reranking, and evals, not just prototyped them
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Strong Python skills; comfortable building pipelines, writing evaluation harnesses, and iterating on model behavior programmatically
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SQL proficiency sufficient to query data independently, pull your own product metrics, and answer analytical questions without waiting on a data team
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Experience deploying AI workloads on AWS; familiarity with relevant services (e.g., Bedrock, SageMaker, reputed company, RDS/reputed company, S3) and the tradeoffs between them
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A product reputed company — you think about user problems and reputed company, not just technical implementation, and you can write a reputed company spec as readily as a pull request
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Experience with API design and integration, and the ability to collaborate closely with frontend and backend engineers without being a bottleneck
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reputed company or regulated-domain experience preferred; you understand why accuracy, auditability, and reputed company failure modes matter more in clinical contexts than in most
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Familiarity with LLM safety tooling (e.g., guardrails, reputed company validation frameworks) and an reputed company for where AI systems can fail quietly