Software Quality Engineer

Adobe•San Francisco, CA

3d•$113,400 - $221,750

About The Position

We’re looking for a technically strong, AI-augmented QE/SDET who can build and scale quality engineering for a rendering-heavy web application. The ideal candidate brings strong QA judgment, understands product risk, and knows how to use AI tooling to accelerate test creation, debugging, triage, documentation, and coverage analysis without sacrificing test reliability. This role focuses on scalable automation, UI testing, visual regression, CI/CD execution, and debugging across React UI, canvas/WebGL/WebGPU surfaces, C++/native rendering layers, cloud-based test artifacts, visual baseline storage, and GPU-enabled test environments.

Requirements

Strong QA judgment with risk-based prioritization.
Ability to identify high-risk rendering paths, async flows, cross-browser surfaces, edge cases, user-impact areas, and gaps between manual coverage and automation coverage.
Comfortable using AI-assisted engineering tools such as Codex, Claude Code, Cursor, GitHub Copilot, or similar systems.
Strong judgment around test signal quality, including the ability to identify flaky tests, weak assertions, noisy coverage, and AI-generated tests that do not validate meaningful product behavior.
Strong experience with visual regression testing for both standard DOM UI and rendering-heavy surfaces such as canvas, WebGL, or WebGPU, including baseline management and diff review.
Experience with test tiering strategies such as smoke, integration, regression, and nightly suites, with the ability to balance coverage, runtime, and PR feedback speed.
Ability to write component-level tests using React Testing Library or Jest.
Understanding of React component lifecycle, state management, and how rendering changes affect the UI.
Experience testing across browsers (Chrome, Firefox, Safari) and screen sizes for cross-browser/responsive rendering issues.
Experience testing canvas/WebGL/WebGPU applications where DOM selectors may not apply, including coordinate-based interactions, timing-sensitive behavior, screenshot validation, state-based validation, and internal state checks.
Familiarity with measuring rendering performance for interactive UI or canvas-based experiences, such as frame rate, frame timing, memory growth, or responsiveness.
Familiarity with CSS-in-JS, Tailwind, or other styling systems to identify layout and style regressions.
Proficient with browser DevTools for inspecting DOM, layout shifts, paint/render performance, and console errors.
Experience using Lighthouse or Web Vitals to assess rendering performance, including LCP, CLS, and INP/FID.
Ability to reproduce and isolate rendering bugs tied to async data loading, animations, or conditional rendering.
Comfortable using AI tools to accelerate log analysis, failure summaries, triage, debugging hypotheses, and root-cause investigation.
Experience writing and running C++ unit and integration tests using frameworks like Google Test (gtest) or Catch2.
Ability to test and validate C++ modules or native bindings that interface with the web frontend (e.g., via WebAssembly or Node.js native addons).
Familiarity with memory management concepts (leaks, dangling pointers, undefined behavior) and tools like Valgrind, AddressSanitizer, or clang-tidy to catch low-level defects.
Experience debugging C++ code using GDB, LLDB, or Visual Studio debugger to trace issues that surface in the rendered UI.
Understanding of how C++ backend or rendering engine code (e.g., a native graphics or layout engine) contributes to frontend output, and how to isolate failures across the stack.
Comfortable reading and reviewing React component code to identify testability gaps or risky rendering logic.
Experience running automated tests in CI/CD pipelines such as CircleCI, GitHub Actions, Jenkins, or similar systems, including visual, functional, and native tests on PRs before merge and scheduled runs against different environments.
Ability to triage bugs across the C++/web boundary and communicate findings clearly to both frontend and systems engineers.
Works closely with developers, designers, PMs, and QA team members to prioritize coverage based on product risk and user impact.

Nice To Haves

Knowledge of agentic testing workflows using tools such as Playwright MCP, custom AI agents, subagents, or similar automation-assistive workflows is a plus.
Familiarity with artifact storage and visual baseline storage using cloud services such as AWS S3, as well as specialized CI environments such as GPU-enabled runners, is a plus.

Responsibilities

Writing and maintaining automated UI test suites using Playwright, Cypress, or Selenium, with Playwright preferred, including scalable architecture such as Page Object Models, fixtures, reusable helpers, and clear locator strategies.
Using AI-assisted engineering tools such as Codex, Claude Code, Cursor, GitHub Copilot, or similar systems to plan, generate, refactor, debug, and validate test code while keeping the output maintainable, trustworthy, and aligned with repo-specific patterns.
Guiding AI output with clear prompts, repo context, coding standards, reusable test patterns, and review loops rather than relying on one-off generated code.
Identifying flaky tests, weak assertions, noisy coverage, and AI-generated tests that do not validate meaningful product behavior.
Performing visual regression testing for both standard DOM UI and rendering-heavy surfaces such as canvas, WebGL, or WebGPU, including baseline management and diff review using tools such as Playwright, Percy, Chromatic, or Storybook to catch unintended rendering changes.
Implementing test tiering strategies such as smoke, integration, regression, and nightly suites, with the ability to balance coverage, runtime, and PR feedback speed.
Writing component-level tests using React Testing Library or Jest.
Testing across browsers (Chrome, Firefox, Safari) and screen sizes for cross-browser/responsive rendering issues.
Testing canvas/WebGL/WebGPU applications where DOM selectors may not apply, including coordinate-based interactions, timing-sensitive behavior, screenshot validation, state-based validation, and internal state checks.
Measuring rendering performance for interactive UI or canvas-based experiences, such as frame rate, frame timing, memory growth, or responsiveness.
Debugging using browser DevTools for inspecting DOM, layout shifts, paint/render performance, and console errors.
Using Lighthouse or Web Vitals to assess rendering performance, including LCP, CLS, and INP/FID.
Reproducing and isolating rendering bugs tied to async data loading, animations, or conditional rendering.
Using AI tools to accelerate log analysis, failure summaries, triage, debugging hypotheses, and root-cause investigation while still validating findings through code, artifacts, and reproducible evidence.
Writing and running C++ unit and integration tests using frameworks like Google Test (gtest) or Catch2.
Testing and validating C++ modules or native bindings that interface with the web frontend (e.g., via WebAssembly or Node.js native addons).
Catching low-level defects using memory management concepts (leaks, dangling pointers, undefined behavior) and tools like Valgrind, AddressSanitizer, or clang-tidy.
Debugging C++ code using GDB, LLDB, or Visual Studio debugger to trace issues that surface in the rendered UI.
Reading and reviewing React component code to identify testability gaps or risky rendering logic.
Running automated tests in CI/CD pipelines such as CircleCI, GitHub Actions, Jenkins, or similar systems, including visual, functional, and native tests on PRs before merge and scheduled runs against different environments.
Triaging bugs across the C++/web boundary and communicating findings clearly to both frontend and systems engineers.
Working closely with developers, designers, PMs, and QA team members to prioritize coverage based on product risk and user impact.