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Noon

AI-driven design-to-production code platform

Lead Quality Assurance Engineer

Test Automation EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, India

About the Job

Requirements

  • Design, build, and drive CI-integrated adoption of a test-automation framework covering web and at least one non-web surface such as desktop, mobile, or API.
  • Maintain strong, current, hands-on TypeScript or JavaScript skills.
  • Have production experience with Playwright, WebdriverIO, Cypress, or an equivalent modern browser-automation stack at meaningful scale.
  • Own CI/CD test-execution infrastructure, including sharding, containerized runs, parallelism, artifact and report plumbing, and flake control.
  • Distinguish tests that protect real behavior from tests that only appear to do so, including mock-heavy versus real-seam testing and outcome assertions versus call assertions.
  • Design a trustworthy, severity-based regression suite using P0-P3 classifications.
  • Hold release gates against real delivery pressure.
  • Have 8-13 years of experience in test automation or quality engineering.
  • Have 3-5 years at Lead, Staff, Test Architect, or Engineering Manager scope, owning both a test framework and a small team of approximately 2-6 people.
  • Have current hands-on TypeScript or JavaScript experience.
  • Have experience with Playwright, WebdriverIO, Cypress, or an equivalent test framework, together with ownership of CI/CD test pipelines.

Responsibilities

  • Convert the highest-priority P0 intent flows across core product families into automated tests using the repository’s existing step-ID grammar, then extend coverage through P1-P3 with severity tagging for modular, on-demand execution.
  • Define standards for test effectiveness based on behavior-driven accuracy, mutation testing, zero-retry reliability, and outcome-based assertions.
  • Automate test-suite execution across every merge and release promotion.
  • Own manual verification of the 25-30 highest-frequency canvas actions and track manual-to-automated drift as a core metric.
  • Reduce regression failures per pull-request merge, keep major P0-P2 defects out of production, and accelerate detection and resolution times.
  • Establish a root-cause-analysis process that requires a deployed preventive measure, such as a new test, review check, or module restriction, before closing an issue.
  • Standardize severity service-level agreements, define the automated-coverage map, optimize merge-versus-promotion gates using data, and pilot AI-driven pull-request risk signals.
  • Build and grow a high-performing quality engineering team.
  • Create an automation framework that developers adopt rather than bypass.
  • Serve as the primary technical owner of test-suite health and the final authority on release quality.

Desired Qualifications

  • Electron or installed-desktop-application automation experience.
  • Mutation testing or another rigorous method for demonstrating that a test suite catches real faults.
  • Visual-regression testing and component-contract testing in a browser runtime, such as Vitest browser mode or Jest with a real DOM.
  • Experience testing complex, stateful user interfaces, including canvases, editors, drag-and-drop, and undo/redo.
  • Test-data and fixture architecture, plus instrumentation of test health such as flake dashboards and coverage-versus-confidence reporting.
  • Root-cause-analysis facilitation and experience mentoring or growing a small team.
  • Experience working in a codebase where a large share of the diff volume is AI-generated.
  • Experience with a product shipped as an installed desktop or mobile client.
  • Exposure to a complex, stateful user-interface product.
  • Testing-craft credentials such as BBST, AST, or similar.

About the Company

Noon

AI-driven design-to-production code platform

Noon builds an AI-native tool that lets designers work directly with a team’s production code, bridging the gap between design and engineering. It uses a dual-canvas so designers shape how a product looks and behaves in the same system, producing output that is actual executable frontend code rather than just visuals. By pulling code from the codebase and design system, it ensures the canvas output matches the shipped product and supports responsive, evolving interfaces. The goal is to help design and engineering teams ship products that reflect the designed experience, with plans for broad access to design teams and a global go-to-market strategy.

Company size
11-50
Company stage
Early VC
Total funding
$44M
Headquarters
San Francisco, CA
Founded
2024

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