Applied AI Engineer

Abby Care is seeking an Applied AI Engineer to build AI-powered products and workflow automation for home care. The role supports solutions across the full lifecycle, from intake and care delivery to billing and compliance, with a focus on production-quality systems.

Key Responsibilities

• Develop agents, copilots, and intelligent workflows that support caregivers, clinicians, and operational teams in delivering care at scale.
• Build backend services, APIs, data pipelines, integrations, and reusable AI components to transition solutions from prototype to reliable production use.
• Create systems that extract, retrieve, and reason over clinical documents, conversations, operational records, and other structured and unstructured data.
• Build representative datasets, measure performance, analyze failures, and improve models, prompts, retrieval, tools, and workflow logic based on evidence.
• Collaborate with Product, Design, Clinical, and Operations to understand real workflows, surface edge cases, and iterate quickly using production feedback.
• Write maintainable, well-tested code and help develop shared capabilities for orchestration, tool use, structured outputs, monitoring, tracing, and evaluation.
• Understand end-to-end workflows by prototyping solutions, preparing datasets, building evaluations, implementing production systems, and learning from real-world usage.
• Collect and interpret clinical documents, automate intake and prior authorization work, assist caregivers and nurses, detect operational or billing risks, and route exceptions to the appropriate person.

Required Qualifications

• 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 engage directly with users and operational workflows.
• Clear written and verbal communication skills.
• Must reside in or be willing to permanently relocate to the San Francisco Bay Area.

Preferred Skills and Experience

• Experience with Python, agent architectures, retrieval-augmented generation, document intelligence, multimodal models, structured generation, model evaluation, monitoring, or prompt optimization is preferred but not required.
• Experience in healthcare, fintech, insurance, another regulated environment, a startup, or an operations-intensive business is preferred but not required.

Technologies

• Python
• Language models
• Machine-learning models
• AI APIs
• Agent architectures
• Retrieval-augmented generation
• Document intelligence
• Multimodal models
• Structured generation
• Model evaluation
• Monitoring
• Prompt optimization

Compensation and Location

Location: San Francisco, CA (hybrid)

Salary: USD 175,000 - 235,000 per yearly

Benefits

• Competitive compensation packages reflecting the value you bring.
• Comprehensive health coverage with a $0 deductible PPO and a company-funded HSA, plus employer-paid life and disability insurance.
• Generous paid time off, including 10 paid company holidays.
• Financial savings benefits including HSA contributions, optional FSA and commuter benefits, and full coverage of all 401(k) account fees (employer match not currently offered).
• Paid parental leave to support your growing family.

What Success Look Like

• Consistently shipping AI-powered capabilities that address real needs for caregivers, clinicians, and operational teams.
• Progressing from prototype to reliable production functionality rather than stopping at demonstration-stage work.
• Building systems supported by clear evaluations, monitoring, and documented limitations.
• Using production feedback and error analysis to improve system quality over time.
• Collaborating effectively with product engineers, domain experts, and operational users.
• Developing reusable components that speed up future Applied AI efforts.
• Growing the ability to independently own increasingly complex projects and technical decisions.
• Building a strong understanding of when to use AI, deterministic software, or human review to produce the best outcome.

Company Values

• Families First
• Urgency with Precision
• Relentlessly Resourceful
• Purpose with Positivity
• Driven to Redefine What’s Possible