QA Engineer

micro1; United States; Remote; USD 60,000 - 90,000; Part time

Job summary

micro1 is seeking a QA Engineer for a remote contractor role focused on AI training data quality. You will design and execute tests to validate AI outputs, annotate datasets to improve model training, and evaluate predictions for errors and patterns.

Collaboration with cross-functional teams will shape project requirements and testing strategies. Ideal candidates have hands-on QA experience, data labeling or RLHF exposure, and English at B2 level or higher to report findings clearly.

Qualifications

• Hands-on expertise in software quality assurance, with test planning and defect reporting.
• Experience with data labeling, annotation, RLHF, or model evaluation.
• Proficiency in English at B2 level or above.

Responsibilities

• Design, execute, and document test cases and scenarios to validate AI outputs against defined requirements.
• Review, annotate, and label data sets to enhance machine learning model training accuracy.
• Evaluate, analyze, and report on model predictions, identifying inconsistencies, errors, or patterns for continuous improvement.
• Collaborate with cross-functional teams to communicate findings and clarify project requirements.
• Contribute to the development and refinement of testing methodologies specific to AI-driven systems.
• Maintain detailed, organized records of evaluations, test results, and feedback through written documentation.
• Provide actionable recommendations to optimize data quality and model performance.

Job description

Role Title: QA Engineer

Role Type: Contractor

Location: Remote

micro1 is engaging QA Engineers to contribute to an AI training project with a forward-thinking customer. 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 ideal for those passionate about quality assurance and eager to be at the forefront of AI innovation, with a focus on data integrity, annotation, and comprehensive model evaluation.

Scope of Work

1. Design, execute, and document test cases and scenarios to validate AI outputs against defined requirements.
2. Review, annotate, and label data sets to enhance machine learning model training accuracy.
3. Evaluate, analyze, and report on model predictions, identifying inconsistencies, errors, or patterns for continuous improvement.
4. Collaborate with cross-functional teams to communicate findings, raise questions, and clarify project requirements.
5. Contribute to the development and refinement of testing methodologies specific to AI-driven systems.
6. Maintain detailed, organized records of evaluations, test results, and feedback through written documentation.
7. Provide actionable recommendations to optimize data quality and model performance.

Preferred Qualifications

1. Hands‑on expertise in software quality assurance, with a strong understanding of test planning and defect reporting.
2. Prior experience with data labeling, annotation, reinforcement learning from human feedback (RLHF), or model evaluation.
3. Proficiency in English at B2 level or above for effective collaboration and reporting.