QA Engineer for AI Model Evaluation

About OpenTrain

OpenTrain AI is the hiring and contracting organization for this remote opportunity. OpenTrain helps people build careers in AI training and data labeling, connecting skilled professionals with projects where human judgment directly improves modern AI systems.

• Remote contractor opportunity open worldwide
• Part-time schedule of less than 20 hours per week
• Expected project duration of 1 to 3 months
• Compensation of $90-$175 per hour

About AI Training and Evaluation

AI training is the human side of building artificial intelligence. Contributors evaluate model responses, provide structured feedback, and prepare high-quality examples that help AI systems learn to reason and perform more reliably.

This project applies software quality assurance expertise to human-in-the-loop AI evaluation. Your testing judgment can help identify outputs that look correct but contain inaccuracies, omissions, or other defects.

• Work with AI-generated technical outputs and evaluation rubrics
• Use professional QA judgment to improve model quality
• No prior AI experience is required beyond qualifying paid human-data experience

The Role

OpenTrain is seeking QA Engineers to contribute software quality evaluation expertise to a high-volume AI training project. You will evaluate and rate AI-generated technical outputs, design and assess software test cases, review defect documentation, and provide precise written feedback.

This opportunity is intended for QA specialists who have both a strong software testing background and prior paid experience with human data annotation, RLHF, model output evaluation, or rubric-based AI grading. Software QA experience alone does not satisfy the project requirement.

• Role: QA Engineer
• Work arrangement: Remote contractor
• Project focus: Software quality assurance and AI evaluation
• Workload: Less than 20 hours per week
• Language: Fluent English, with written communication at B2 level or above

What You'll Do

You will apply structured QA methods to evaluate technical model outputs and software-testing materials. Strong documentation and actionable feedback are essential, since developers should be able to understand and address findings without additional clarification.

• Evaluate and rate AI-generated technical outputs against defined quality criteria and rubrics.
• Identify responses that appear correct but are inaccurate, incomplete, or otherwise deficient.
• Design and assess functional, regression, edge-case, negative, and boundary test cases.
• Review bug reports and test documentation for reproducibility, completeness, and accurate severity assessment.
• Identify, isolate, and document defects with precise reproduction steps.
• Use structured bug-tracking and test-management processes.
• Provide detailed, actionable written feedback and annotations for developers.
• Collaborate with project teams to refine evaluation guidelines and improve testing standards.

Required Qualifications

The project is listed as entry level, and no formal degree is required. However, applicants must demonstrate practical software testing experience and meet the firm requirement for prior paid human-data work supporting AI training.

• Professional experience as a QA Engineer, SDET, Test Engineer, QA Analyst, or in a similar software quality role.
• Strong knowledge of test case design, bug tracking, regression testing, and manual and automated testing.
• Prior paid human-data experience in annotation, labeling, RLHF, AI response evaluation, model evaluation, or rubric-based grading.
• Hands-on experience with automation frameworks and test-management tools.
• Excellent analytical and problem-solving skills with meticulous attention to detail.
• Clear written English communication and the ability to provide specific, actionable findings.
• Reliable internet connection and readiness to begin promptly.
• Fluent English proficiency.

Tools and Testing Skills

Relevant experience may include the following tools or comparable alternatives. The core requirement is practical ability to perform and document quality assurance work across manual, automated, regression, and defect-evaluation workflows.

• Selenium, Playwright, Cypress, Appium, Postman, or BrowserStack
• Jira, TestRail, Zephyr, or similar test-management and bug-tracking tools
• Manual and automated testing
• Test case design and regression testing
• Bug reporting and defect isolation
• Quality evaluation and human-data annotation

How to Apply Through OpenTrain

Create a free OpenTrain account and apply in minutes. Highlight your software QA background, testing tools, automation experience, and qualifying paid experience with AI evaluation or human-data annotation so the project team can assess your fit.

• Review the project details and contractor terms.
• Showcase relevant QA and AI evaluation experience in your OpenTrain profile.
• Apply for consideration for this remote, part-time project.