Superscript's engineering organisation is growing and adapting. Our engineers are increasingly building alongside AI copilots and agentic tooling, and we're excited about what that unlocks: more ambition, faster delivery, and the chance to take on problems that would have been out of reach a year ago.
As we grow, we're investing in the foundations that will let us move quickly and confidently. Quality is one of those foundations. We believe the best version of quality isn't a final check before release. It's a system that gives every engineer the tooling, standards and feedback loops to own the quality of what they build. That's the next chapter for quality at Superscript, and we're looking for someone to help us write it.
This is our first dedicated Quality Engineering hire, and it's a genuine shaping role. We see the future of quality at Superscript as a blend of disciplines, with QE and QA working alongside each other and bringing distinct strengths to the table. You'll partner with our existing QA colleagues to shape what that blended landscape looks like in practice, and help the wider engineering org embrace quality as a shared responsibility. You won't be inheriting a QE function. You'll be helping design the quality model we need for the next stage of our growth.
In practice, that means:
Defining what great quality looks like at Superscript, and building the shared language around it.
Shaping tooling, standards, and automation patterns with a real blank canvas to work with.
Bringing teams with you through coaching, credibility and influence.
Exploring what Quality Engineering looks like in an org where humans and AI agents are increasingly shipping code side by side, and helping set the direction others will learn from.
If you love rolling up your sleeves and shaping something from the ground up, this is the role for you.
Strengthen the system that produces quality. Define clear expectations for reliability and release confidence. Build the practices and guardrails that turn those expectations into how squads plan, build and release by default. Continuously improve how quality is measured and made visible, so teams know where they stand without having to ask you.
Enable engineers to own quality. Establish a quality framework, shaped with engineers, that the team can adopt, contribute to, and make their own. Introduce lightweight risk assessment into planning and refinement. Remove the bottlenecks (manual regression, end-of-sprint validation, "waiting for QA") that slow teams down and push quality to the end.
Align validation with risk and business impact. Make sure the depth of testing matches the complexity of the change and the criticality to the business. Turn defect and incident trends into preventative improvements. Influence the architectural and process decisions that shape reliability upstream.
Build the QE practice for an AI-enabled engineering org. Our engineers increasingly work with AI copilots and agentic tooling. You'll shape how we validate AI-generated code, design guardrails that let teams move fast safely, and evaluate emerging tools (AI-assisted test generation, synthetic data, and more) with a clear-eyed view of what signal they actually add.
You don't need to tick every box, but the people who will do well here tend to have:
High agency, above all. You observe, orient, decide, and act in fast loops, setting your own direction, and treat ambiguity as something to shape rather than a reason to stall.
Strong automation engineering craft. You've designed and scaled test automation frameworks that real teams rely on, and you know what makes them survive contact with production.
A risk-based testing mindset. You can articulate what (and what not) to test, when, and why, and you're comfortable pushing back on testing that adds ceremony without signal.
A lifecycle view of quality. CI/CD and observability are part of it, but you see quality as something that lives from refinement through to production monitoring, not just a green check in a test run.
Experience shifting testing left. And, importantly, experience coaching engineering teams to own it rather than delegating it back to you.
The temperament to lead through influence. Calm during incidents, credible in tough conversations, allergic to blame culture, and able to build ownership without creating dependency.
A curious, experimental attitude to AI in engineering workflows. You see agentic tools as something to shape and evaluate, not something to either fear or uncritically adopt.
A competitive salary and the ability to grow your career
25 days holiday - so there’s plenty of time for work, rest and play
Hybrid way of working for the perfect work / life balance
Vitality private health and dental insurance
Added benefits including competitive pension and salary sacrifice
Access to financial wellbeing platform Mintago
Full access to Spill, our mental health platform
Cycle to work scheme
Fun and modern office environment with regular team social events
City of London location
