QA Engineer - Remote

YO AI Labs; New York (NY); Remote; USD 55,000 - 103,000; Full time

Job summary

YO AI Labs is seeking a detail-oriented QA Engineer to support an AI training project. This contractor role is remote and focuses on quality assurance, data annotation, and AI model evaluation.

You will design and execute test cases, review datasets, identify errors in model predictions, document results, and collaborate with teams to improve testing methods and data quality. English proficiency at B2 or higher is required.

Qualifications

• Experience in software testing and quality assurance.
• Understanding of test planning and defect reporting.
• Experience with data annotation, RLHF, or AI evaluation is preferred.
• Strong analytical, documentation, and communication skills.

Responsibilities

• Design and execute test cases to validate AI outputs.
• Review, annotate, and label datasets.
• Identify errors and inconsistencies in model predictions.
• Document test results and provide actionable feedback.
• Collaborate with teams to improve testing methodologies.
• Recommend improvements to data quality and model performance.

Skills

Software Quality Assurance

Test Planning and Test Case Design

Data Annotation and Labeling

AI Model Evaluation

Defect Reporting and Documentation

Data Quality and Validation

Analytical Thinking and Attention to

English Communication (B2 or higher)

Job description

QA Engineer

Role Type: Contractor
Location: Remote

Job Description

We are seeking detail-oriented QA Engineers to support an AI training project focused on quality assurance, data annotation, and AI model evaluation. Prior AI experience is not required.

Required Skills

• Software Quality Assurance
• Test Planning and Test Case Design
• Data Annotation and Labeling
• AI Model Evaluation
• Defect Reporting and Documentation
• Data Quality and Validation
• Analytical Thinking and Attention to Detail
• English Communication (B2 or higher)

Responsibilities

• Design and execute test cases to validate AI outputs.
• Review, annotate, and label datasets.
• Identify errors and inconsistencies in model predictions.
• Document test results and provide actionable feedback.
• Collaborate with teams to improve testing methodologies.
• Recommend improvements to data quality and model performance.

Qualifications

• Experience in software testing and quality assurance.
• Understanding of test planning and defect reporting.
• Experience with data annotation, RLHF, or AI evaluation is preferred.
• Strong analytical, documentation, and communication skills.