Director Engineering - AI/ML

Stanford Health Care•Palo Alto, CA

3d•$112 - $149•Hybrid

About The Position

Stanford Health Care and the Stanford School of Medicine are at the forefront of the AI revolution in healthcare. We are building chatEHR, a secure, compliant, and powerful Generative AI platform designed to transform healthcare delivery, accelerate medical research, and improve patient outcomes. We are seeking a hands-on, visionary Director of Engineering to lead the technical development and strategic execution of the chatEHR platform. This is a unique opportunity to build and scale a team that sits at the intersection of pioneering AI research and real-world clinical application. You will be responsible for building the core platform, agentic frameworks, and user-facing applications that will be used by thousands of clinicians, researchers, and staff.

Requirements

10+ years of experience in software engineering, with at least 5+ years in a senior leadership role (Manager, Senior Manager, or Director) managing software development teams.
Proven track record of leading teams that have successfully built, deployed, and scaled AI/ML products.
Deep technical knowledge of the modern GenAI stack, including LLMs, retrieval-augmented generation (RAG), vector databases, and agentic frameworks.
Expertise in designing and building scalable, cloud-native systems (GCP, AWS, or Azure) and container orchestration (Kubernetes).
Strong experience with API design, microservices architecture, CI/CD, and MLOps principles.
Exceptional ability to mentor engineers, set technical direction, and manage complex projects with multiple stakeholders.
Outstanding communication and interpersonal skills, with the ability to articulate complex technical concepts to non-technical and clinical audiences.
BS or MS in Computer Science, AI, or a related engineering field. Required

Nice To Haves

Direct experience in the healthcare, biotech, or life sciences industry, particularly with standards like HIPAA, FHIR, or GxP.
Experience building applications on top of Electronic Health Record (EHR) systems like Epic.
A portfolio of published research in AI/ML or contributions to major open-source projects.
PhD in Computer Science, AI, or a related field. Preferred

Responsibilities

Build & Mentor: Recruit, lead, and mentor a high-performing team of software engineers and data scientists, fostering a culture of innovation, collaboration, and technical excellence.
Define the Vision: Partner with clinical and product leadership to develop and execute a strategic technical roadmap for the chatEHR platform, aligning with organizational goals.
Ensure Compliance & Ethics: Serve as the key engineering leader responsible for ensuring all AI applications adhere to strict healthcare regulations (HIPAA), data privacy laws, and ethical AI standards.
Platform Architecture: Oversee the architecture and design of a scalable, secure, and reliable GenAI platform, ensuring robust integration with existing EHR systems and clinical workflows.
Agentic Frameworks: Lead the design and development of a sophisticated agentic framework for Stanford Healthcare, enabling rapid creation and deployment of new AI-powered workflows.
API & Application Development: Manage the development of user-facing chatEHR applications and publish a comprehensive set of platform APIs for enterprise-wide consumption.
Release Management: Manage multiple concurrent development streams and releases, ensuring timely delivery and high-quality standards for the entire platform.
Modern MLOps: Implement and oversee robust MLOps practices, including state-of-the-art GenAI evaluation techniques in collaboration with the Chief Data Science Officer and the members of the GUIDE-AI lab ([Link to arXiv paper]).
Bridge Research & Practice: Act as the primary technical liaison between your engineering team and partners in the Chief Data Science Officer's lab, translating novel research into production-ready platform features.
Engage Stakeholders: Work closely with cross-functional teams—including product management, user success, clinical staff, and researchers—to identify and prioritize high-impact opportunities for GenAI.