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Abby Care

Applied AI Engineer

San Francisco (Remote)fulltimemidAdded today

About this role

Abby Care seeks an Applied AI Engineer to build AI-powered products and workflow automation for their home care platform. You'll develop production systems that help caregivers, clinicians, and operational teams deliver better care—moving beyond prototypes to reliable, observable solutions that handle clinical documents, intake automation, prior authorization, and operational risk detection.

What you'll do

  • Build AI agents, copilots, and intelligent workflows that support caregivers, clinicians, and operational staff
  • Develop production-ready backend services, APIs, and data pipelines to move AI solutions from prototype to reliable use
  • Extract, retrieve, and reason over clinical documents and unstructured healthcare data
  • Create datasets, measure performance, analyze failures, and improve models and prompts based on evidence
  • Partner with Product, Design, Clinical, and Operations teams to understand real workflows and iterate on feedback
  • Write maintainable, tested code and contribute to shared AI orchestration and monitoring capabilities

What they're looking for

  • Large Language Models and prompt engineering
  • LLM application frameworks (LangChain, LlamaIndex, or similar)
  • Python backend development
  • Data pipeline design and processing
  • Evaluation and testing of AI systems
  • API development and integrations
  • SQL and healthcare data handling
  • Production monitoring and observability
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Abby Care

Abby Care builds AI-powered products designed to support family caregivers in delivering quality home care. The company is hiring Full-Stack Engineers to own features end-to-end, working with modern cloud and AI technologies across clinical and operational teams.

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Likely interview questions

  • Walk us through a project where you took an AI prototype to production—what were the main challenges and how did you handle reliability and monitoring?
  • Describe your approach to evaluating AI system quality when ground truth is unclear or expensive to obtain.
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