Senior Director, Quality Engineering

Spring Health is a global mental health company on a mission to eliminate every barrier to mental health. We're building a world where getting support is simple, personal, and built around the person, so care can continue through every job, move, health plan, and life stage.

Our AI-native platform helps us deliver personalized support across self-guided tools, coaching, therapy, medication management, and specialty care. With outcomes independently validated by JAMA Network Open and the Validation Institute, Spring Health reaches more than 170 million people worldwide through leading employers, health plans, and partners.

As an AI-native company, we believe technology should expand the reach, quality, and humanity of care. Every Spring Health team member is expected to use AI tools thoughtfully, apply human judgment to AI outputs, and keep building AI fluency in ways that support their role and our mission.

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.

• →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.

• 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.
• Industry-leading testing frameworks supporting AI-native applications, LLM-powered features, and autonomous agent workflows.
• AI agents successfully augment Quality Engineering through autonomous test generation, execution, defect analysis, and release intelligence.
• Engineering teams adopt quality-by-design principles, resulting in faster release cycles without compromising reliability.
• Comprehensive trust, safety, and responsible AI validation processes protecting customer experience and regulatory compliance.
• Quality is a shared responsibility, engineering teams own their quality outcomes, supported by QE platforms and standards rather than dependent on manual QA gates.
• A high-performing Quality Engineering organization recognized as a strategic partner enabling product innovation rather than slowing delivery.

• Proven experience leading and scaling quality engineering organizations (multiple teams across products) in high-growth & fast paced environments, including distributed and globally sourced teams.
• Deep technical expertise in test automation architecture at scale & modern frameworks (e.g., Playwright, Cypress, pytest, Jest), CI/CD integration, contract and API testing, performance and resilience testing.
• Hands-on experience building evaluation and testing systems for AI/ML or LLM-powered products: eval harnesses, golden datasets, LLM-as-judge, regression testing across model and prompt changes, red-teaming, and guardrail validation.
• Demonstrated experience deploying AI agents or AI-assisted tooling into the SDLC, test generation, triage, maintenance, or code review with measurable productivity gains.
• Strong track record of shift-left transformation: moving organizations from manual QA gatekeeping to engineer-owned quality with platform support.
• Experience with quality in regulated or high-consequence domains (healthcare, fintech, or similar), including HIPAA/SOC 2, PHI-safe test data, and audit and traceability requirements.
• Strategic mindset with the ability to balance long-term vision and hands-on execution; credible in deep technical design reviews.
• Strong cross-functional leadership and communication skills, with the ability to influence at executive levels and make quality trade-offs legible to non-engineering stakeholders.
• Demonstrated ability to drive measurable outcomes (e.g., escaped defect rate, change failure rate, test cycle time, automation coverage, eval pass rates, cost per release).

The target base salary range for this position is $230,000 - $250,000, and is part of a competitive total rewards package including equity and benefits. Individual pay may vary from the target range and is determined by a number of factors including experience, location, internal pay equity, and other relevant business considerations. We review all employee pay and compensation programs annually using Radford Global Compensation Database at minimum to ensure competitive and fair pay.