Manager, Quality Assurance

Manager, Quality Assurance

Most QA leaders inherit a process and make it a little better. That's not what we're hiring for.

We're looking for a Manager, Quality Assurance who can fundamentally rethink how software quality should work in an AI-enabled engineering organization.

We already have a QA team, automation, established processes, and a functioning software delivery model. We're not looking for someone to simply maintain it. You'll be responsible for taking our QA organization to the next level — and then continuing to raise the bar after you get there.

That means challenging old assumptions about manual testing, regression testing, test case creation, defect triage, release validation, automation maintenance, quality gates, and even the traditional role of QA itself.

We believe AI creates an opportunity to dramatically improve how software quality is achieved. Not by replacing good people or good judgment, but by giving exceptional people far more leverage. We want a leader who sees that opportunity and knows how to turn it into reality.

You'll own RTA's quality standards and QA processes and be accountable for the quality of our software as measured by clearly defined KPIs. Quality is a shared responsibility across Engineering, and you'll help build the systems, standards, and practices that make that ownership possible.

What We're Looking For

Someone who:

• Wants to reinvent QA, not preserve it. You question processes that exist simply because "that's how QA has always worked."
• Makes quality measurable. You use meaningful metrics such as escaped defects, repeat defects, defect origin, severity, automation effectiveness, regression cycle time, and time-to-detection to understand where we are improving and where we need to change.
• Sees AI as a force multiplier. You actively look for ways AI can improve test design, code analysis, exploratory testing, defect investigation, automation, risk identification, and release confidence.
• Uses AI as leverage, not a substitute for judgment. You understand where AI can dramatically accelerate the team and where experienced human judgment is still critical. You expect the team to validate outputs, understand what they're doing, and remain accountable for quality.
• Thinks beyond test execution. You understand that mature quality practices influence requirements, architecture, development, observability, deployment, and production behavior.
• Builds systems that make quality scalable. Your instinct is not to add more testers or more manual steps. It is to improve tooling, automation, feedback loops, and engineering practices.
• Sets a high bar — and then raises it. You are never satisfied simply because a metric improved. You continually ask what "great" looks like next.
• Experiments aggressively. You are comfortable trying new AI tools, workflows, agents, testing approaches, and automation techniques — keeping what works and quickly abandoning what doesn't.
• Builds and develops a high-performing QA team. You set clear expectations, coach and develop your people, hold the team accountable for results, and help them grow beyond traditional manual testing into automation, analysis, tooling, systems thinking, and engineering.
• Uses data to challenge assumptions. You want evidence that quality is improving, not anecdotes.
• Is comfortable disrupting your own process. If the approach you implemented six months ago is no longer the best answer, you change it.
• Takes ownership. You don't wait for Engineering leadership to tell you what QA should become. You help define it.

Most importantly, you care deeply about our customers. Our customers rely on Fleet360 to manage school buses, emergency vehicles, public works fleets, transit vehicles, and other critical assets. Software quality has real-world consequences, and we take that seriously.

Your Top 3

If you join RTA, these are the three things we'll look back on and say, "Yep, that's why we hired you."

1. You Built Quality In

The first thing we point to: customers stopped finding our defects.

You moved quality upstream — into requirements, design, development, and code review — so fewer defects were ever created, and the ones that were got caught long before a customer saw them.

You treated our escape rate as yours. Not a team's, not a release's. Ours.

How we'll know:

• Customer-reported defects have fallen materially — and are still falling.
• QA is engaged before code is written, not handed a build at the end.
• More defects get caught in requirements, pull requests, and CI than in a test cycle.
• Acceptance criteria and testability got better because you pushed on them.
• Engineering owns quality alongside QA — and you're the reason that shifted.

2. You Automated Everything That Repeats

Manual regression stopped being how we validate software.

You went after repetitive human validation with AI, automation, and better tooling until it became the exception rather than the operating model — and our coverage kept growing while the hours required to hold it shrank.

How we'll know:

• Manual regression hours per release have dropped sharply — without escapes rising.
• Automated coverage concentrates on the workflows and risks that actually matter, not raw test count.
• AI is standard practice in test design, edge-case discovery, defect investigation, and automation maintenance.
• Tests are cheaper to write and cheaper to maintain; brittle, redundant, and low-value tests are gone.
• You challenged the tooling whenever something better existed — including tooling you chose.

3. You Fixed It Once

Defects stopped coming back.

When you arrived, a bug got fixed and nothing else changed. You built the learning loop we didn't have, so every escape changed something structural — a process, a gate, a pattern, or a design decision.

How we'll know:

• Defects stopped recurring in the same areas and from the same root causes.
• Fixes hold: reopens and fix-induced regressions have declined.
• Every escape produced a documented root cause and a change to process, tooling, coverage, or design.
• Release readiness is expressed as evidence and known risk, not gut feel.
• Leadership can see quality by release, team, and product area — and see it improving.

Your First 60 Days

You will have diagnosed where RTA's current QA model is limiting quality or speed, earned the team's trust, and put at least one meaningful AI- or automation-driven improvement into production that measurably raises the bar.

Qualifications

• 5+ years of software quality assurance or quality engineering experience, preferably within a SaaS organization.
• 2+ years leading or managing QA or quality engineering professionals.
• A demonstrated track record of materially improving QA processes, teams, automation, or quality outcomes.
• Strong experience designing and implementing modern software testing strategies.
• Experience building, managing, or significantly improving automated testing programs.
• Experience with tools such as Playwright, Katalon, Cypress, Selenium, or similar frameworks.
• Strong understanding of API testing and modern web application architectures.
• Strong understanding of how QA fits throughout the software development lifecycle.
• Experience defining and using quality metrics to identify trends and drive improvement.
• Strong analytical, troubleshooting, and root-cause analysis skills.
• Hands-on experience using AI tools to improve software quality or engineering workflows is strongly preferred.
• The ability to challenge existing practices, drive organizational change, and bring people along with you.

The Bottom Line

You've made it this far, so congratulations!

We are not hiring someone to run our existing QA department. We are hiring someone