Remote AI QA Engineer - Data and Model Testing (Canada)

Jobgether; Kanada; Remote; CAD 87.000 - 165.000; Vollzeit

Benefits dieser Stelle

Competitive hourly compensation

Remote work flexibility

Contract type: Freelance / independent

Contract duration: 12 months

Working hours: 40 hours per week

Start date: As soon as possible

International collaboration

Zusammenfassung

Jobgether, on behalf of a partner, is seeking a QA Engineer based in Canada to join a remote, international team. You will design and execute structured tests for AI-driven systems, assess model outputs, and document results to improve accuracy and data quality.

Your responsibilities include annotating and labeling datasets, evaluating predictions, reporting findings, and helping develop testing methodologies.

Qualifikationen

• Hands-on software quality assurance experience with testing practices.
• Strong analytical skills and attention to detail when reviewing results.
• Ability to identify inconsistencies, investigate issues, and communicate findings.
• English proficiency at B2 level or above for collaboration and reporting.
• Solid written documentation skills and independent work style in a remote setup.

Aufgaben

• Design, execute, and document test cases to validate AI outputs.
• Annotate and label datasets to improve ML training data quality.
• Evaluate model predictions to identify errors and patterns.
• Report findings and propose improvements to data quality and model performance.
• Collaborate with cross-functional teams to clarify requirements.
• Contribute to testing methodologies for AI-driven systems.
• Maintain organized documentation of evaluations and recommendations.

Kenntnisse

Test planning

Defect reporting

Data quality analysis

AI testing

Model evaluation

Remote collaboration

Attention to detail

Jobbeschreibung

Jobgether, on behalf of a partner, is seeking a QA Engineer based in Canada to join a remote, international team. You will design and execute structured tests for AI-driven systems, assess model outputs, and document results to improve accuracy and data quality.

Your responsibilities include annotating and labeling datasets, evaluating predictions, reporting findings, and helping develop testing methodologies.