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Applied AI Engineer

Abby Care
CompanyAbby Care
CategoryEngineering
LocationSan Francisco
RemoteHybrid
EmploymentNot stated
LevelNot stated
SalaryUSD 165k–220k
Posted16 Jul 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
ABOUT ABBY CARE: POWERING THE FUTURE OF CARE AT HOME FOR ALL OF AMERICA. Abby Care is building the leading AI-native platform for family-led care. America is facing a growing care crisis. Millions more people need care at home than ever. Over 50 million family caregivers support loved ones without the tools, training, or recognition they deserve. We believe families are the largest untapped caregiving workforce in America, and that technology can help them deliver better care while driving stronger outcomes and greater transparency across the healthcare system. Abby Care combines clinical oversight with an AI-powered platform to train, enable, and support family caregivers in delivering high-quality care at home. Our platform helps health plans and government partners better understand, verify, and improve care in the home. We expand access to care, reduce reliance on higher-cost settings, and help ensure public dollars are spent effectively. We are proud to partner with leading health plans, providers, and community organizations and are backed by top VCs. We envision a future where family-led care is a core part of the healthcare system. Abby Care is building that future. Join us in solving one of the most important challenges of our time. WHAT YOU’LL WORK ON Build AI-powered products and automation: Develop agents, copilots, and intelligent workflows that help caregivers, clinicians, and operational teams deliver better care at scale. Develop production systems: Build the backend services, APIs, data pipelines, integrations, and reusable AI components required to move solutions from prototype to reliable production use. Work with complex healthcare information: Create systems that extract, retrieve, and reason over clinical documents, conversations, operational records, and other structured and unstructured data. Evaluate and improve system quality: Build representative datasets, measure performance, analyze failures, and improve models, prompts, retrieval, tools, and workflow logic based on evidence. Learn directly from users and operations: Partner with Product, Design, Clinical, and Operations to understand real workflows, identify edge cases, and rapidly iterate based on production feedback. Contribute to strong engineering foundations: Write maintainable, well-tested code and help build shared capabilities for orchestration, tool use, structured outputs, monitoring, tracing, and evaluation. WHAT SUCCESS LOOKS LIKE - You consistently ship AI-powered capabilities that solve real problems for caregivers, clinicians, and operational teams. - Your work progresses from prototype to reliable production functionality rather than stopping at demonstration-stage experiments. - The systems you build are supported by clear evaluations, monitoring, and documented limitations. - You use production feedback and error analysis to improve system quality over time. - You collaborate effectively with product engineers, domain experts, and operational users. - You develop reusable components that accelerate future Applied AI work. - You grow your ability to independently own increasingly complex projects and technical decisions. - You build a strong understanding of when to use AI, deterministic software, or human review to produce the best outcome. WHAT YOU’LL HAVE - 2+ years of professional software engineering, machine learning, or Applied AI experience, or equivalent demonstrated ability. - Strong programming skills and software engineering fundamentals. - Experience building applications using language models, machine-learning models, or modern AI APIs. - Familiarity with backend development, APIs, data processing, testing, and production software systems. - Ability to structure ambiguous problems, test assumptions, and iterate based on evidence. - Interest in building evaluations and understanding why AI systems succeed or fail. - Strong ownership, curiosity, and willingness to
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