Define and own the product vision, strategy, and roadmap for Clinical AI tools with no PM layer above you.
Architect, implement, and continuously improve the RAG infrastructure powering clinical decision support.
Design and iterate on prompt strategies, system instructions, and guardrails to produce reliable, clinically appropriate outputs.
Deploy and maintain scalable, HIPAA-compliant AI infrastructure on AWS.
Work with healthcare data sources and integrate AI outputs into clinical workflows.
Own the relationship with clinicians and clinical operations directly with no PM intermediary.
Partner with backend and frontend engineers to integrate AI capabilities into the broader product.
Actively track developments in applied AI research and bring relevant advances to bear on the product.
Translate technical AI concepts for clinical and operational stakeholders and write actionable documentation.

Requirements

5–8 years of experience in software or AI/ML engineering, with a meaningful portion in applied AI product development
Hands-on experience building and operating RAG systems in production — you’ve made real decisions about chunking, embeddings, retrieval design, reranking, and evals, not just prototyped them
Strong Python skills; comfortable building pipelines, writing evaluation harnesses, and iterating on model behavior programmatically
SQL proficiency sufficient to query data independently, pull your own product metrics, and answer analytical questions without waiting on a data team
Experience deploying AI workloads on AWS; familiarity with relevant services (e.g., Bedrock, SageMaker, Lambda, RDS/Aurora, S3) and the tradeoffs between them
A product mindset — you think about user problems and outcomes, not just technical implementation, and you can write a clear spec as readily as a pull request
Experience with API design and integration, and the ability to collaborate closely with frontend and backend engineers without being a bottleneck
Healthcare or regulated-domain experience preferred; you understand why accuracy, auditability, and safe failure modes matter more in clinical contexts than in most
Familiarity with LLM safety tooling (e.g., guardrails, output validation frameworks) and an instinct for where AI systems can fail quietly

Benefits

💻 Fully remote opportunity
🩺 Medical, dental, and vision insurance
📈 401K retirement plan with company match
🏝️ Up to 20 days of PTO per year + company holidays
👶 Up to 14 weeks of parental leave (12 for non-birthing parents)
🏡 Annual work from home stipend for remote employees

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSQLRAG systemsAI/ML engineeringAPI designmodel behavior iterationdata queryingoutput validation frameworksclinical decision supportscalable AI infrastructure

Soft Skills

product mindsetcommunicationcollaborationproblem-solvingdocumentation writingstakeholder engagementdecision-makinguser outcome focustechnical translationiterative design