Quality Engineer (Automation & AI)

About Zyte: Zyte (zyte.com) is a globally established data engineering innovator, web scraping infrastructure pioneer, and AI-native web harvesting provider operating on an absolute mission to convert the unorganized internet into structured, production-grade intelligence. For over a decade, our cloud proxy networks, headless browser APIs, and automated data harvesting engines have powered data operations for global search aggregators, enterprise market researchers, and software platforms worldwide. We view quality not as a final gatekeeping blockade, but as a rigorous software engineering discipline built directly into our deployment pipelines. Zyte equips forward-thinking engineering builders with an uncompromised software canvas to develop autonomous quality gates, scale agentic testing frameworks, and leverage AI coding assistants within highly distributed, remote-first squads.

Position Overview

We are seeking a highly analytical, connection-obsessed, and systems-minded Quality Engineer to join our unified Quality Engineering function under a permanent, full-time remote engagement framework open to talented developers globally. Operating as an embedded engineer directly within our cross-functional product squads, you will take true individual operational and strategic accountability over accelerating software delivery speeds while systematically driving defect rates to zero. Shifting completely away from traditional manual spreadsheet verification, slow post-development bug reporting, or passive execution logging, you will run an active testing framework engineering, runtime debugging, and agentic validation laboratory—partnering face-to-face with developers, product owners, and software architects. This position requires an automation veteran who designs risk-based testing paths fluidly natively using Automation Engineer principles, configures self-healing regression architectures cleanly natively leveraging AI coding assistants (such as Claude Code), and commands complex end-to-end integration workflows confidently inside containerized CI/CD release tracks.

Key Responsibilities

• AI-Native Coverage Strategy: Architect and deploy robust, risk-based automated verification matrices for complex feature sets, translating core functional specs into rapid automated coverage via AI-assisted code generation.
• Agentic Integration Engineering: Design and execute agentic testing workflows that interact directly with LLM APIs to generate, adapt, and autonomously heal automated scripts across system upgrades.
• Playwright Framework Governance: Code, expand, and maintain enterprise-grade automated test suites natively utilizing Automation Engineer paradigms, embedding rigorous quality gates straight into GitHub CI/CD pipelines.
• Shared Productivity Tooling: Build modular testing utilities, custom assertion frameworks, and automated developer platform libraries that double test authoring velocity across distinct product squads.
• Shift-Left Pipeline Architecture: Collaborate intimately with product managers and backend developers early in design phases to influence requirements, system observability properties, and progressive deployment blueprints.
• Suite Reliability Triage: Eliminate runtime flaky signals, track test execution times, optimize backend container runtimes, and track escape defect analytics to safeguard build confidence.

Required Skills & Qualifications

• Strong software engineering fundamentals, demonstrating complete comfort reading, refactoring, and debugging programmatic logic across modern backend or frontend tech stacks.
• Extensive hands-on expertise building and maintaining scalable automation frameworks from scratch, with deep technical mastery over Playwright.
• Proven capacity running modern AI coding agents (e.g., Claude Code, Cursor, GitHub Copilot) as a primary interface to write, optimize, and document robust automation code natively using Automation Engineer models.
• Solid operational history setting up and monitoring automated regression testing suites directly within modern CI/CD orchestration layers like GitHub Actions.
• Clear, scannable technical communication style enabling seamless collaboration across asynchronous, internationally distributed engineering squads.
• Location Context: Position is 100% remote-first, allowing experienced automation specialists to work from anywhere globally while maintaining high engineering standards.

Preferred Strategic Indicators (Nice to Have)

• Prior history testing data extraction software, AI-powered systems, or intricate LLM API integrations within production environments.
• Experience designing custom validation or comparison engines to monitor and evaluate non-deterministic AI-generated outputs.
• Familiarity with foundational prompt engineering patterns, LLM observability frameworks, or modern performance, resilience, and heavy-load testing practices.
• Background building internal developer platforms (IDPs) or specialized engineering productivity frameworks.

What We Offer

• Vetted, Advanced AI-Native Developer Blueprint: An exceptionally competitive global compensation architecture matched accurately to your technical depth and framework engineering mastery.
• The incredible professional canvas to pioneer agentic, self-healing test architectures using cutting-edge AI coding toolchains.
• Profound work-from-home remote parameters offering a 100% virtual environment, absolute daily schedule execution autonomy, and zero geographical office bottlenecks.
• A flat, agile development ecosystem focused heavily on technical innovation, open-source adoption, and early preventative engineering.
• Substantial professional trajectory advancement by shaping testing standards for a world leader in web data collection systems.