QA Lead - Quality Engineering & AI Testing

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/dialects to engage individuals across voice, SMS, and email, and 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 US, helping millions of members access the support they need to stay healthy. We don’t just offer software—we deliver impact.

About The Role

As we scale, we’re looking for an ambitious and highly technical QA Lead to build and elevate Blooming Health’s quality engineering function through our next phase of growth. This is a US-based remote role with required overlap on EST hours.

This QA Lead will serve as both a hands-on technical quality leader and people manager, partnering closely with Engineering, Product, and AI teams to improve product quality, strengthen QA operational maturity, and scale testing practices across a rapidly evolving SaaS platform. This is not a purely managerial role — we are looking for someone who can actively contribute to test strategy, automation, tooling, and quality initiatives while also leading and developing a distributed QA team.

The ideal candidate brings strong QA engineering fundamentals, startup execution experience, and the ability to thoughtfully evolve what modern QA should look like in an AI-driven engineering environment.

What You'll Do

QA Strategy & Roadmap: Define and drive the QA roadmap across the organization. Establish scalable quality processes, testing standards, metrics, and operational practices that improve product reliability, release confidence, and engineering velocity.

Hands-On Technical Leadership: Contribute directly to test planning, automation strategy, debugging, exploratory testing, and quality investigations. Partner with engineers to deeply understand architecture, systems behavior, and product functionality.

Team Leadership & Resource Allocation: Lead, mentor, and manage the QA engineering team, including performance management, workforce planning, prioritization, and resource allocation across multiple initiatives and product areas.

Quality Process Improvement: Evaluate and improve existing QA workflows, automation coverage, release processes, defect management, and overall SDLC quality practices. Introduce measurable improvements to testing efficiency and product quality.

AI-Enabled Testing Practices: Leverage modern AI tooling and agentic workflows (Claude, Gemini, etc.) to accelerate test generation, planning, automation, and QA productivity. Help define how AI can responsibly and effectively modernize QA practices.

Cross-Functional Partnership: Build strong working relationships across Engineering, Product, AI, and Customer-facing teams. Act as a trusted quality partner who helps balance speed, reliability, and user experience.

Operational Excellence: Establish quality metrics, reporting, and accountability mechanisms. Help create visibility into product quality trends, release readiness, production risks, and testing effectiveness.

Secure & Compliant Development Support: Ensure QA practices appropriately support secure software development and high standards around sensitive and regulated data environments.

Requirements

What We're Looking For

• Strong QA Engineering Expertise: Deep experience in software quality engineering, including test strategy, automation frameworks, debugging, exploratory testing, and modern QA methodologies.
• Hands-On Technical Capability: Comfortable actively contributing as an individual contributor while leading a team. Able to review architecture, understand systems deeply, and collaborate closely with engineers on technical quality initiatives.
• People Leadership & Resource Management: Experience managing QA engineers, including performance management, mentorship, resource allocation, prioritization, and workforce planning.
• Startup/Growth-Stage Experience: Proven track record delivering results in fast-paced startup or growth-stage SaaS environments where processes are evolving and teams must balance speed with quality.
• AI-Assisted QA Workflow Experience: Ability to leverage modern AI tools and agents to improve QA productivity, including generating test plans, accelerating automation, and enhancing testing workflows.

Browse our curated collection of remote jobs across all categories and industries, featuring positions from top companies worldwide.

• Technical Curiosity & Systems Thinking: Strong ability to deeply understand application architecture, APIs, backend systems, and engineering tradeoffs. Preference for candidates comfortable working in open-source-oriented engineering environments.
• Cross-Functional Collaboration: Excellent communication and partnership skills with Engineering, Product, and leadership stakeholders.
• Experience Working with Secure Data: Familiarity operating in environments involving sensitive or regulated data and secure development practices.
  
  

Preferred Qualifications

• Healthcare Technology Experience: Experience within healthcare technology, HIPAA-regulated environments, or other highly regulated industries is strongly preferred.
• Modern Automation & AI Testing Practices: Familiarity with evolving QA approaches in AI-enabled development environments, including intelligent test generation and automation workflows.
• Open Source Engineering Environments: Experience working within highly technical engineering organizations that emphasize deep system understanding, source code review, and open-source tooling ecosystems.
• Distributed Team Collaboration: Experience working with distributed engineering and QA teams across multiple locations and time zones.
• Technical Breadth: Comfortable working across modern SaaS architectures, APIs, backend systems, and automated testing frameworks in language-agnostic environments.