Senior Engineering Manager, Quality

Doxy.me

20h

About The Position

This role leads a lean, high-leverage engineering capability focused on production quality and incident response. You'll set the quality philosophy for doxy.me's engineering organisation, own the Test and Rapid Response engineering, and shape how engineering teams think about risk, confidence, and speed. This is not a role for someone looking to build a bigger QA practice with more process and more tests, but rather an engineering leader who has thought deeply about what quality actually means when software development is being reshaped by AI, and has a clear thesis about what comes next.

Requirements

Engineering management depth — you've led teams, owned their output, and understand how engineering orgs work
Real quality engineering expertise — unit, integration, E2E, contract, exploratory — and crucially, when each doesn't earn its place
Honest experience with AI-assisted testing — LLM-based generation, intelligent test selection, coverage analysis — including where current tooling falls short
Strong opinions on what healthy product testing looks like
Able to articulate quality ROI in business terms
Strong data literacy
Familiarity with modern automation frameworks and CI/CD tooling

Nice To Haves

You're a software engineering manager first, with real depth in quality engineering.
You understand test strategy, but you're not defined by it.
You have a clear point of view on why traditional QA structures are mostly broken, and you can articulate what replaces them.
You've made the call to delete more tests than you've written.
You've seen coverage metrics lie.
You know the difference between a quality function that creates confidence and one that just creates ceremony.
You have genuine conviction about AI-augmented engineering — not because it's trendy, but because you've seen what's possible when intelligent tooling handles the toil and engineers focus on judgment.
You have real opinions on where LLM-based test generation works, where it doesn't, and what a genuinely AI-first approach to quality looks like in practice.
You think about org design as much as test design. How do you build a small, high-leverage team that punches well above its weight?
How do you build quality thinking into engineering culture before code is written, not just after?
Those questions excite you.

Responsibilities

Define what quality means at doxy.me — ground strategy in Provider and Patient outcomes across web, mobile, and backend.
Hold the org to signals that reflect real customer impact, not coverage volume.
Lead Test Engineering — Test Engineers report to you, matrixed into product squads. Set the standard for how they operate, coach their growth, and define how quality embeds into daily delivery as AI reshapes how software is built and tested.
Own Rapid Response — triage, prioritise, and drive resolution of high-priority production issues end-to-end. Set SLAs, run the process, keep the team steady under pressure, and be the clear bridge between what customers are experiencing and what engineering is doing about it.
Audit existing test practices without sentiment — identify low-value, slow, and bloated coverage. Make the tough calls to cut it, even when it's unpopular.
Set the philosophy for AI-augmented testing — define how intelligent tooling, automated maintenance, and risk-driven coverage should work at doxy.me. Free engineers to focus on judgment, not mechanical upkeep.
Shape engineering enablement — work with EMs and Staff Engineers to identify where shared standards multiply delivery quality across every squad.
Coach on quality thinking — help engineers and managers understand what good looks like and why coverage volume isn't the goal.