Product Engineer - AI/ML

Stanford Health Care • Palo Alto, CA

7d • $82 - $108 • Hybrid

About The Position

The Product Engineer – AI/ML bridges technical strategy, hands-on software development, and applied AI delivery. In a forward-deployed capacity, the role works directly with operational and clinical stakeholders to uncover root challenges, prototype solutions, and build high-value AI capabilities.

The role owns end-to-end execution, including writing production-capable code, configuring AI model workflows such as RAG and foundation APIs, and driving initial 0-to-1 implementation. Working alongside Data Scientists and Core Platform Engineering, the Product Engineer helps ensure AI solutions deliver measurable impact.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related quantitative field, or equivalent practical experience.
• 3–5+ years of progressive experience in data science, software engineering, applied AI engineering, or technical product development.
• Experience working in healthcare, health tech, or complex regulated enterprise environments.
• Excellent communication, interpersonal, problem-solving, and analytical skills.
• Knowledge and understanding of healthcare informatics technologies and standards.
• Proven ability to manage complex projects with multiple stakeholders and competing priorities.

Nice To Haves

• Master’s degree.
• Proficiency in Python and SQL; experience with REST APIs, web services, and modern development workflows.
• Hands-on experience with foundation model APIs, Retrieval-Augmented Generation (RAG), vector databases, and AI orchestration frameworks.
• Familiarity with enterprise data structures, cloud data platforms, and data privacy governance.
• Ability to analyze operational workflows, probe for root causes, and translate business needs into technical solutions.

Responsibilities

• Partner with operational and clinical teams to identify root-cause challenges rather than passively receive feature requests.
• Design, build, and iterate on working prototypes using programming languages such as Python, modern APIs, LLM orchestration frameworks, and retrieval systems.
• Evaluate operational bottlenecks and determine whether automation, rule-based logic, or generative AI is the best technical approach.
• Balance technical tradeoffs involving model accuracy, execution latency, maintainability, and operational inference costs.
• Integrate prototypes with health system data stores, enterprise APIs, and operational workflow tools.
• Collaborate with Data Science teammates to pass solutions through formal evaluation harnesses before enterprise deployment.
• Help construct test suites to measure and mitigate model hallucinations, omissions, and factual inaccuracies.
• Ensure alignment with healthcare data privacy policies, HIPAA regulations, and institutional AI governance standards.
• Partner with Data and Software Engineers to transition validated prototypes to enterprise-grade platforms.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level