Senior AI Quality Engineer

About Lendable

Lendable is on a mission to build the world's best technology to help people get credit and save money. We're building one of the world’s leading fintech companies and are off to a strong start:

• One of the UK’s newest unicorns with a team of just over 700 people

• Among the fastest-growing tech companies in the UK

• Profitable since 2017

• Backed by top investors including Balderton Capital and Goldman Sachs

• Loved by customers with the best reviews in the market (4.9 across 10,000s of reviews on Trustpilot)

So far, we’ve rebuilt the Big Three consumer finance products from scratch: loans, credit cards and car finance. We get money into our customers’ hands in minutes instead of days.

We’re growing fast, and there’s a lot more to do: we’re going after the two biggest Western markets (UK and US) where trillions worth of financial products are held by big banks with dated systems and painful processes.

Join us if you want to

1. Take ownership across a broad remit. You are trusted to make decisions that drive a material impact on the direction and success of Lendable from day 1

2. Work in small teams of exceptional people, who are relentlessly resourceful to solve problems and find smarter solutions than the status quo

3. Build the best technology in-house, using new data sources, machine learning and AI to make machines do the heavy lifting

We're hiring two Senior Quality Engineers to set up a Quality Engineering department at Lendable alongside a new Head of Quality.

At Lendable we already have a number of profitable, award winning credit products. They have a good automated quality story. However, as our customer base has grown into the millions we need to keep evolving to keep the quality bar high. Advances in AI engineering also have enabled us to ship faster, but we need to ensure we are not regressing on our quality metrics.

This is an automation-first role. You'll spend most of your time writing code - AI Agents, test frameworks, CI tooling, helpers. You won't be executing tests by hand. You'll work cross-functionally with software engineers, product and design. Quality is a team-level responsibility and your job is to work with teams to enable them to improve their quality processes by hands-on collaboration.

You'll also be working at the frontier of AI-assisted quality, using LLMs and AI tooling to speed up test authoring, triage failures, and surface coverage gaps, while applying the engineering judgement to keep tests trustworthy.

What you'll be doing

Enable teams to keep the quality bar high

• Rotating around different product teams for short periods to level up quality

• Define and evolve the pragmatic test pyramid for our products - deciding where E2E is worth the weight and where testing belongs in faster layers (unit and component tests)

• Make deliberate calls about coverage, reliability, and speed trade-offs across our products

• Devise and set up performance benchmarks to ensure critical parts of our systems don't regress

Bring AI testing into the SDLC

• Speed up our SDLC by building agents that exploratory test changes and catch issues before they get to production

• Use AI tools day-to-day to accelerate test authoring, cluster failures, surface coverage insights, and propose fixes

• Decide where AI adds leverage and where a human eye is needed. A test suite that looks comprehensive but isn't trustworthy is worse than a smaller one we rely on

Build reliable E2E automation

• Discover gaps in testing and help teams build comprehensive suites

• Get test environments and data fixtures to a level where they are easy to use and reliable

• Drive flakiness down and time-to-diagnosis down. We treat a flaky test the same way we treat a broken test, and we expect root-cause work rather than retry-until-green

Prevent defects, not just catch them

• Partner with product and engineering on shift-left quality: join specs early, push back on ambiguous acceptance criteria, and surface risk before code is written

• Close the loop on production issues using our observability stack (Datadog, Sentry, Grafana) - tying test coverage back to real customer impact

• Ensure teams have Service Level Objectives set up and are achieving them

• Run targeted exploratory testing on high-risk releases when it's the right call

What we're looking for

Essential

• 5+ years in quality engineering or test automation, with a track record of keeping automated suites stable rather than letting them rot into a graveyard of quarantined tests

• Experience working in a scaled engineering environment with multiple teams pulling in different directions and priorities, and know how to navigate that to drive quality improvements across them

• Hands-on coding experience of writing test code, frameworks, CI pipelines and other helper scripts. You know what a good test codebase looks like and you constantly improve it

• API and contract testing experience, with a point of view on test data and environment strategy

• Have set up and used Service Level Objectives to define a contract of system reliability with stakeholders, and driven measurable improvements against them

• Native AI-assisted working style. You can describe projects where AI meaningfully changed how you worked - test authoring, failure triage, coverage analysis - and where you decided it wasn't the right tool

• Coaching other disciplines in quality. You evangelise quality practices and get others on board to solve quality problems together

• Experience shaping CI/CD pipelines for test reliability (GitHub Actions or similar), including flakiness reduction, parallelisation, artefact management, and runtime control

• Proactive, low-ego, and clear communicator, able to chase things down when blocked

• Able to operate independently and drive improvements end-to-end (frameworks, CI, test data, reporting)

• Can point to 3-4 projects where you moved the needle on quality - for example improving SLO coverage or reducing incident rate - and can speak to the measurable impact of each

Nice to have

• Have worked with an incident management process to root cause issues with engineers and devise meaningful quality improvements as a result

• Experience in PHP, Kotlin or Typescript

• Experience in financial services (retail or institutional) or other regulated environments

Our tech stack

Frontend: TypeScript, React, React Native

Backend: PHP (Symfony), Kotlin, AWS, Postgres, RabbitMQ, Docker, Kubernetes

Testing: PHPUnit, Behat, JUnit, Kotest, Jest, Maestro, K6

Tooling and observability: GitHub, GitHub Actions, Jira, Confluence, Datadog, Sentry, Grafana

Why join?

• See your work matter: Our products are used by millions of customers - the quality bar you help set has a direct line to customer trust and company revenue

• High impact: You'll decide where and how to engage across teams to move quality forward, working hands-on rather than through proces