Abby Care Senior Applied AI Engineer at Abby Care designing and building production AI systems to automate complex healthcare workflows. Owns initiatives from problem definition through deployment and impact measurement.
Responsibilities
Abby Care is building an AI-native operating system for home care—one that manages the full lifecycle from caregiver and patient intake through care delivery, billing, and compliance.
As we expand our capacity to support more family caregivers at scale, we’re looking for a Senior Applied AI Engineer to design and build production AI systems that automate complex, consequential healthcare workflows. Reporting to the Applied AI Leader, you will own major initiatives from initial problem definition through production deployment and measurable impact.
Depending on the problem, you may build an agent that collects clinical documentation, interprets evidence across multiple records, prepares a prior authorization packet, assists a nurse during a visit, identifies billing risk, or coordinates operational follow-up.
High-agency and pragmatic, you are comfortable operating in ambiguity, moving between experimentation and production engineering, and owning technically complex systems end to end. If you are excited to apply frontier AI capabilities to difficult, high-impact problems while transforming care in the home, we’d love to hear from you!
This is a full-time hybrid opportunity based in San Francisco, California, with four days per week in person.
Own high-impact AI systems end to end: Lead initiatives from workflow discovery and technical design through production deployment, iteration, and measurable business or care outcomes.
Build reliable agents and copilots: Develop systems that reason over structured and unstructured data, use internal tools, and execute multi-step workflows across intake, care delivery, billing, and operations.
Engineer for real-world complexity: Design systems that handle incomplete information, conflicting evidence, changing requirements, and operational edge cases by combining models, retrieval, rules, state machines, and human oversight.
Build rigorous evaluation and monitoring: Create representative datasets, evaluation harnesses, production tracing, confidence-based escalation, and systematic error-analysis processes.
Integrate AI into complete workflows: Partner with Product, Design, Clinical, Operations, Analytics, Platform, and System of Record engineers to ensure AI capabilities solve real problems rather than remain isolated prototypes.
Raise the technical bar: Contribute reusable platform capabilities, shape architectural decisions, mentor engineers, and improve how the team builds and operates Applied AI systems.
