Senior QA Automation Engineer

(united States)Remotesenior

EngineeringQa Engineer

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Quick Summary

Key Responsibilities

Framework Development: Design, build, and maintain scalable end-to-end (E2E) automation frameworks using Playwright (or Selenium) to support complex web applications and APIs.

Technical Tools

EngineeringQa Engineer

Requirements

~1 min read

Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Experience: 5+ years of dedicated professional experience in software QA automation engineering.
Automation Tools: Proficient with Playwright (TypeScript, JavaScript, or Python); hands-on experience with Selenium is acceptable if accompanied by a strong understanding of modern testing architectures.
AI/LLM Competency: Demonstrated experience working with Large Language Models (specifically Claude or OpenAI APIs) and writing effective system prompts for technical use cases.
Project Tools: Experience using Linear for agile issue tracking, bug lifecycle management, and sprint planning is highly preferred (Jira experience is acceptable).
CI/CD Skills: Practical knowledge of integrating automated tests into deployment pipelines.
Agile Mindset: Strong ability to thrive in a fast-paced, iterative Agile/Scrum development environment.

Basic understanding of AI driven QA Approaches.
Exposure to self healing Test Automation
Excellent analytical and problem-solving abilities.
Strong written and verbal communication skills, with a keen attention to detail.
Proven leadership skills with the ability to manage and motivate a team.
Experience working with team members located in different time zones.

Responsibilities

~1 min read

→Framework Development: Design, build, and maintain scalable end-to-end (E2E) automation frameworks using Playwright (or Selenium) to support complex web applications and APIs.
→AI & LLM Integration: Incorporate Anthropic Claude (or other LLMs) into the test suite to enable advanced capabilities like self-healing scripts, non-deterministic UI validation, and automated test data generation.
→Workflow Optimization: Leverage prompt engineering and LLM APIs to automate repetitive QA tasks, optimize test coverage, and handle dynamic content validation.
→Project & Bug Management: Track defects, manage regression suites, and drive sprint tasks within Linear to maintain high visibility of product quality.
→CI/CD Automation: Integrate automated test execution pipelines into CI/CD workflows (e.g., GitHub Actions, GitLab CI) for seamless continuous testing.
→Cross-Functional Collaboration: Partner with developers, product managers, and data scientists to identify risk early in the product lifecycle and align on validation strategies.