Quality Engineering Lead (Automation & AI Testing) - Mobile (React Native)

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 maintain the quality bar high. Advances in AI engineering have enabled us to ship faster, but we need to ensure we are not regressing on our quality metrics.

Our mobile app, Zable, is how most of our customers experience all of our products in one centralized space. It's written in React Native and TypeScript with a Kotlin backend, shipped to iOS and Android from a single codebase. We are now looking to level up the quality engineering so that we can raise release cadence without losing sleep over regressions.

This is an automation-first role. You'll spend most of your time writing code—test frameworks, CI tooling, helpers—rather than executing test scripts by hand. You'll work alongside software engineers, product, and design, setting direction.

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 engineering judgment to keep tests trustworthy.

Own the mobile test strategy:

• Define and evolve a pragmatic test pyramid for React Native—deciding where End-to-End (E2E) is worth the weight and where logic belongs in faster layers (Jest, React Testing Library, component tests).
• Make deliberate calls about coverage, reliability, and speed trade-offs across the app.
• Set the bar for what “ready to ship” looks like on mobile, and hold the line on it.

Enable teams to keep the quality bar high:

• Rotate 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 Software Development Life Cycle (SDLC) by building agents that explore 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 (SLOs) set up and are achieving them.
• Run targeted exploratory testing on high-risk releases when it's the right call.