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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

  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.

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