Applied AI Engineer

Role overview Build and operate production AI agents and assistants that support real users in healthcare infrastructure. The role is open at Senior, Staff, and Senior Staff levels, with level matched to the scope and depth of experience. It combines AI system design with production engineering, evaluation, security, and close work with operational and clinical partners.

Responsibilities - Build and run LLM agents, including the runtime, tool use, context handling, handoffs, retries, timeouts, and sandboxing. - Create evaluation data and test systems to measure quality, catch regressions, and show whether changes improve performance. - Support multiple model providers, manage version changes, and plan fallbacks for provider failures. - Trace agent behavior in production, monitor it, and attribute AI costs to agents and use cases. - Ground agent responses in business data using retrieval, context design, and prompt engineering. - Work with operations teams to identify suitable workflow steps for automation and when a case should go to a person. - Protect patient information, control agent access, and address misuse such as prompt injection; collaborate with data platform teams on governed data. - Contribute to code and design reviews, mentor engineers newer to AI, and explain tradeoffs to technical and nontechnical partners.

Requirements - Bachelor’s degree in computer science, engineering, or a related technical field. - Five or more years of software engineering experience. - Hands-on experience building and operating production LLM systems relied on by users; Senior Staff candidates typically have at least two years in this area. - Depth in at least one of agent runtime, evaluation, retrieval, or observability and cost. Staff and Senior Staff candidates should bring depth in agent runtime or evaluation; Senior Staff candidates should have a second deep area and working knowledge across the others. - Experience evaluating AI systems with data, plus strong production skills across testing, debugging, services, APIs, deployment, and on-call. - Experience handling sensitive data with a security-first mindset, and clear communication with technical and nontechnical partners.

Nice to have Experience in healthcare, finance, or insurance; production services on Google Cloud; model or provider migrations; machine learning beyond LLMs; internal engineering platforms; or, for Senior Staff, setting technical direction for and growing teams.

Benefits and work setup Salaried benefits include medical, dental, and vision coverage; flexible spending or health savings accounts; flexible paid time off; a 401(k) with company match; and life and pet insurance.