Director of QA

About Blooming Health

Blooming Health is on a mission to transform social care. Our AI-powered platform identifies social needs and barriers by continuously collecting real-time data from member interactions and screenings. It then drives action through automated, empathetic outreach in 55 languages and dialects across voice, SMS, and email, coordinating referrals to the right services at the right time.

The result is improved access to care, higher program participation, and better outcomes across diverse populations. Our platform supports 1,000 community organizations across the U.S., helping millions of members access the support they need to stay healthy. We don’t just offer software — we deliver impact.

As we scale, we’re looking for an ambitious and highly technical Director of QA to lead Blooming Health’s quality strategy, QA organization, automation framework, and release-readiness processes.

This is a senior engineering leadership role for someone who can operate at the intersection of quality engineering, test automation, platform reliability, AI-enabled product validation, compliance, and engineering execution. The ideal candidate has deep experience building scalable QA processes for complex B2B SaaS platforms and can partner closely with Engineering, Product, Data, AI, Security, and Customer Success to ensure we deliver reliable, secure, high-quality products at speed.

This role is U.S.

-based, with preference for candidates working on EST or CST.

The Director of QA will be responsible for evolving our QA function from manual/reactive testing into a mature, automation-first, data-driven quality organization that supports rapid product development, enterprise customer expectations, and regulated healthcare environments.

Define and own Blooming Health’s overall QA strategy across web applications, backend services, APIs, integrations, data workflows, AI-enabled features, and multi-product platform capabilities.

Build and lead a scalable QA function that supports multiple engineering pods and product work streams.

Establish quality standards, test strategies, release gates, defect management processes, and QA metrics across the engineering organization.

Partner with Engineering and Product leadership to ensure quality is built into the SDLC from planning through release and production monitoring.

Design and mature an automation-first QA framework across UI, API, integration, regression, performance, and end-to-end testing.

Increase test coverage, reduce manual regression burden, and improve release confidence.

Implement best practices for CI/CD-integrated automated testing, test data management, environment management, and quality reporting.

Evaluate and implement QA tools, frameworks, and processes that improve engineering speed, predictability, and product reliability.

Own QA readiness for major releases, roadmap commitments, customer launches, integrations, and platform changes.

Partner with engineering teams to identify risks early, prevent escaped defects, and improve delivery predictability.

Drive improvements in defect triage, root cause analysis, regression planning, and production issue prevention.

Help teams move faster without compromising quality, security, compliance, or customer trust.

Develop QA strategies for AI-enabled and agentic product experiences, including workflow automation, outreach logic, conversational flows, recommendations, and data-driven decisioning.

Partner with AI, Data, Product, and Engineering teams to validate model behavior, output quality, guardrails, edge cases, monitoring, feedback loops, and production reliability.

Ensure AI-powered features are tested for accuracy, consistency, safety, usability, explainability, and customer impact.

Support quality processes for data pipelines, integrations, analytics workflows, and member interaction data.

Partner with Engineering and Dev Ops to improve observability, monitoring, incident response, SLOs/SLAs, and production health.

Ensure QA…