QA Engineer
micro1 · 100% remote · Contract · Posted
- Pay
- $63–120/hr
- Location
- Worldwide
- Languages
- English
- Hours
- Flexible
- Openings
- 120
- Level
- Experienced
Summary
Design and execute test cases for AI outputs, annotate data, evaluate model predictions, and document findings to improve AI model training and performance.
What you'll do
- Design, execute, and document test cases and scenarios to validate AI outputs
- Review, annotate, and label data sets for machine learning training
- Evaluate, analyze, and report on model predictions
- Collaborate with cross-functional teams to communicate findings
- Contribute to developing testing methodologies for AI-driven systems
- Maintain detailed records of evaluations and test results
- Provide actionable recommendations to optimize data quality and model performance
Requirements
- Have hands-on expertise in software quality assurance
- Understand test planning and defect reporting
- Have prior experience with data labeling, annotation, RLHF, or model evaluation
- Proficiency in English at B2 level or above
Skills
- Software Quality Assurance
- Test Case Design
- Test Execution
- Defect Reporting
- Data Annotation
- Data Labeling
- AI Model Evaluation
- Documentation
- English Communication
- Cross-Functional Collaboration
Full 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
- 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, raise questions, 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.
Preferred Qualifications
- Hands-on expertise in software quality assurance, with a strong understanding of test planning and defect reporting.
- Prior experience with data labeling, annotation, reinforcement learning from human feedback (RLHF), or model evaluation.
- Proficiency in English at B2 level or above for effective collaboration and reporting.
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