Quality Engineer

About hyperexponential (hx) At hyperexponential, we're building the AI-powered platform that enables the world's most critical decisions in a $7 trillion industry, which risks to take, and how to price them. These are the decisions that shape real-world outcomes: whether rockets successfully launch into space, autonomous vehicles make it to market, or communities recover after major storms. Until now, insurance has been making billion-dollar decisions using outdated tools. We're changing that. Our platform brings together data, AI, and human expertise to give insurers the fastest path from submission to decision - helping them move faster, act smarter, and take on more risk with confidence. Backed by a16z, Highland Europe, and Battery Ventures, we're scaling globally - already trusted by nearly 50 of the world's largest insurers, with zero churn and billions in premiums flowing through hx. What began as a single product in one market has rapidly evolved into a multi-product, multi-territory platform powering every stage of pricing and underwriting. AI is at the core of what we do - from building the world's first domain-specific AI peer programmer for insurance (think GitHub Copilot with a PhD in actuarial science) to shaping agentic workflows that reinvent how this industry operates. What makes hx different is the people who build it. Here, impact isn't tied to title or tenure; it's defined by the challenges you take on and the discipline you bring. Surrounded by peers who stretch you, you'll do the best, hardest work of your life in a company engineered to endure. If that sounds like you, join us in building what comes next. About the team Quality Engineering at hx is undergoing a strategic shift. We're moving from an embedded model, where QEs sit in product teams and deliver hands-on testing, to an enablement model, where a small central team builds the tools, standards, and coaching that help every engineer own quality. It's one of hx's boldest engineering experiments: making quality genuinely self-service without sacrificing the rigour our customers rely on. The team sits at the intersection of test automation, AI tooling, release readiness, and organisational change. We're building AI-powered systems that make end-to-end test creation accessible to all engineers, automate failure classification and routing, and provide the standards, guidance, and feedback loops teams need to make better quality decisions independently. That means shaping quality strategy, improving release risk assessment, and establishing lightweight, scalable processes for staging, deployment, and learning from failure. This is strategic work with company-wide impact, requiring technical depth, strong technical judgement, coaching capability, and comfort with ambiguity. As the third member of this newly formed team, you'll join at a pivotal moment. You'll contribute to the managed transition away from embedded QE support, take ownership of AI-powered automation tools still being defined, and build the foundations that allow hx to scale quality across dozens of engineering teams. Your work will shape whether teams can ship independently, whether release processes stay fast and trusted as we grow, and whether hx can maintain velocity without compromising reliability. Check out our Engineering Candidate Hub for a behind the scenes look at the team you would be joining! What you'll be doing Accelerate the development and adoption of hx's AI-powered end-to-end test writing agent, improving its accuracy and framework coverage to the point where at least 15 engineers across 5+ teams are independently writing E2E tests within six months of widespread release. Design and deploy an AI-powered nightly build analyser that automatically classifies test failures and routes them to owning teams, reducing daily manual review effort by 80% and proving reliable enough to extend to the release pipeline. Establish quality self-service foundations for product teams by delivering test design guidance, executable E2E framework documentation (as AI agent specifications), quality standards references, and release risk assessment templates that enable at least three teams to complete release assessments without direct QE involvement. Support the managed withdrawal of QE support from embedded product teams by contributing to fixed-scope deliverables that ensure no critical gaps in E2E coverage or delivery confidence, enabling both current QEs to transition on schedule with no unplanned regressions. Grow E2E test coverage across the organisation through coaching, pairing, and AI tooling support, increasing the number of product teams writing their own E2E tests to at least 50% within the first 12 months. Build credibility and influence across engineering teams by working alongside engineers to solve real quality challenges, demonstrating the value of team-owned quality through concrete examples rather than process mandates, and making quality ownership feel l