Quality Assurance Engineering Manager

We are standing up a dedicated QA automation function, and we're looking for a QA Engineering Manager to build and lead it from the ground up. This is a rare opportunity to shape a quality practice from scratch: you'll choose the tooling, set the standards, hire the team, and define how AI fits into how we test.

This is a hands-on, player-coach role. You'll split your time roughly 50/50 between leading the team and doing the work yourself: setting direction, hiring, and coaching on one side, and writing automated tests, building framework components, and reviewing code on the other. You'll lead by example and stay close enough to the code to make sound calls and earn the trust of the engineers you lead.

In your first 90 days, you'll start with discovery: partnering with our Principal AI Engineer and AI team to understand existing AI tools and practices, and working with engineering teams to understand current QA practices and define how QA and engineering will collaborate going forward. In parallel, you'll confirm prerequisites (environments, CI/CD, critical user journeys), select your tooling and framework, and stand up a proof-of-concept suite on the highest-risk flows. By the end of year one, you'll have delivered a working, trusted automation suite on critical flows, a small high-performing team, a quality metrics dashboard in active use, and a clear plan for AI adoption.

What You Will Do

What You Bring

• 7+ years in software quality or engineering, including 3+ years leading and growing engineers.
• A track record of building or significantly maturing a test automation practice.
• Current, hands-on coding ability: you can build framework components and write automated tests yourself, and you want to keep doing so in this role.
• Strong technical judgement on automation frameworks, CI/CD, and test architecture, backed by recent hands-on experience.
• Fluency with modern automation tooling (Playwright, Cypress, Selenium) and CI/CD (GitHub Actions, Jenkins, GitLab CI).
• Experience defining quality metrics and using them to influence release decisions.
• Clear communicator who can translate quality risk into terms leadership and product can act on.
• Hands-on understanding of how AI is changing testing, with sound judgement on where it helps and where it doesn't. Experience evaluating or adopting AI-assisted QA tooling or a credible plan for doing so.
• Familiarity with testing AI or LLM features (evaluation harnesses, golden datasets, guardrails, bias and accuracy checks), or the ability to get there quickly.

Nice to Have

Our Hiring Process: 

👋 Stage 1: Recruiter Interview (45 minutes)

We'll dive into your professional background and experience, share more about SpryPoint and the role, and answer any burning questions you might have.


👋 Stage 2: Hiring Manager Interview (45 minutes)

This is a great chance to meet with the Director of Software Engineering and dive deep into your career goals, work experience, and how we can support your growth. We encourage you to ask questions to learn more about the opportunity.


🧑‍💻 Stage 3: Technical Assignment

You'll complete a technical exercise relevant to the role.


👥 Stage 4: Final Interviews (60 minutes)

You'll walk the team through your technical assignment and showcase your skills. This is also a great opportunity to meet more of the team and see if there's a strong team fit!

🚀 Stage 5: References and Offer

We'll check your references to confirm all the great things we've learned about you. If all goes well, we’ll be thrilled to extend an offer and welcome you to the team!