QA Engineer

Role Title: QA Engineer

Role Type: Contractor

Location: Remote

micro1 is engaging QA Engineers to contribute expertise in software quality evaluation for a customer's high-volume project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. This opportunity is open to specialists with strong professional QA backgrounds. Prior paid experience with human data annotation, RLHF, model output evaluation, or rubric-based grading for AI training is a plus, but not required. Scope of Work

Evaluate and rate AI-generated technical outputs against defined quality criteria and rubrics, identifying answers that may appear correct but are actually inaccurate or incomplete. Design and assess comprehensive test cases for functional, regression, and edge-case scenarios, including negative and boundary tests. Review bug reports and test documentation to ensure issues are reproducible, documentation is complete, and severity is properly assessed. Identify, isolate, and document defects with precise reproduction steps and utilize structured tracking mechanisms. Provide detailed, actionable written feedback and annotations to support efficient resolution by developers without the need for clarification. Collaborate with project teams to refine evaluation guidelines and ensure continuous improvement of testing standards and methodologies.

Preferred Qualifications

Proven professional experience as a QA Engineer, SDET, Test Engineer, QA Analyst, or similar role in software quality assurance. Strong command of QA fundamentals, including test case design, bug tracking, regression testing, and both manual and automated testing strategies. Hands-on experience with automation frameworks and test management tools such as Selenium, Playwright, Cypress, Appium, Postman, Jira, TestRail, Zephyr, or BrowserStack (or similar tools). Prior paid human data experience supporting AI training — annotation, labeling, RLHF, AI response evaluation, model evaluation, or rubric-based grading — is a plus. Excellent analytical and problem-solving skills with meticulous attention to detail. Ability to communicate complex findings clearly in written English, providing specific, actionable feedback (B2 level or above). No formal degree required—practical, demonstrable testing experience takes precedence. Reliable internet connection and readiness to begin promptly.