Senior Director, Quality Engineering

As a senior engineering leader, you will play a pivotal role in shaping and executing the quality strategy for everything we ship. You will define what "high quality" means for an AI-native healthcare company — building the testing frameworks, agentic quality systems, and evaluation infrastructure that let us release faster with more confidence, and that earn clinical and enterprise trust in AI-driven features.

Reporting to the SVP of Engineering, you will lead the Quality Engineering organization responsible for driving org-wide testing strategy, intelligent automation, and AI-powered quality practices across the software development lifecycle. You will partner closely with Engineering, Product, Design, Infrastructure, Security, and Data Science leaders to embed quality throughout the development process, not simply validate software before release.

You will help redefine software quality through agentic AI by deploying autonomous testing agents, intelligent regression systems, and AI-driven quality insights that accelerate delivery while improving confidence in every release. This leader will establish modern testing frameworks for AI-native products, large language model (LLM) powered features, and intelligent workflows, ensuring they meet the highest standards for accuracy, robustness, security, trust, and responsible AI.

Please note that this is a hybrid role based in San Francisco, with an expectation to be in the office 2–3 days per week at our 44 Montgomery Street location. Candidates must be based in the San Francisco metro area or able to relocate independently within 90 days of their start date. Occasional travel will be required for team on-sites.

What you'll do

• Contribute to company-wide leadership initiatives including workforce planning, org design, and build-vs-buy decisions on quality tooling and vendors. Define, instrument, and report the quality metrics that leadership and the Board use to assess engineering health.
• Define and execute the enterprise Quality Engineering strategy, embedding quality across the AI-native SDLC through automation, measurable quality standards, and release governance.
• Build modern testing frameworks for AI-powered products, LLM applications, and agentic workflows, including automated evaluation, model validation, and continuous quality monitoring.
• Drive adoption of AI agents to automate test generation, regression testing, defect analysis, release readiness, and engineering productivity.
• Establish scalable testing practices across functional, integration, regression, performance, security, accessibility, and end-to-end testing to improve release confidence and reduce production defects.
• Build reusable testing platforms, automation frameworks, and CI/CD quality tooling that enable engineering teams to own quality and accelerate delivery.
• Define validation frameworks that ensure AI systems are reliable, safe, compliant, and trustworthy while partnering with Security, Privacy, Clinical, and AI teams.  Ensure testing practices satisfy HIPAA, SOC 2, and applicable clinical and regulatory requirements, validation evidence, auditability, traceability, and PHI-safe test data management.
• Lead and scale a high-performing, distributed Quality Engineering organization while driving cross-functional alignment and fostering a culture of quality, ownership, and continuous improvement.

What success looks like

• Enterprise-wide quality strategy fully integrated into the AI software development lifecycle.
• Significant improvements in automated test coverage across functional, integration, regression, performance, security, and AI-specific testing.
• High release confidence with measurable reductions in escaped defects, production incidents, and customer-reported issues.<