As a Machine Learning SQA Engineer, you will design and execute test automation and validation systems that ensure the reliability, accuracy, and consistency of AI and ML-based applications. You’ll help bridge classic software QA principles with emerging AI quality challenges — validating large language model (LLM) outputs, data pipelines, and AI-driven APIs.
This role is ideal for a QA engineer with strong automation skills and growing experience in AI or data-centric systems, who is eager to apply proven testing discipline to next-generation intelligent products.
Role expectations
Key Responsibilities
Develop, maintain, and execute automated and manual test cases for AI/ML systems, APIs, and data pipelines.
Build and enhance test automation frameworks to validate LLM or ML model outputs and integrations.
Test AI agent workflows and monitor system performance, reliability, and output consistency.
Collaborate with engineers and data scientists to diagnose, document, and track defects.
Evaluate LLM and prompt behavior against predefined benchmarks and edge cases.
Participate in code reviews and provide feedback on testability and quality standards.
Contribute to continuous integration and delivery (CI/CD) testing processes and pipelines.
Maintain test documentation, datasets, and metrics to track system health and quality trends.
Research and propose new QA methodologies for AI and ML systems (“AI testing AI”).
What Were Looking For
Required Skills & Experience
5-8 years of experience as an SQA, Test Automation, or QA Engineer (preferably with exposure to data or ML systems).
Proficiency in Python and one or more of: JavaScript, Java, or TypeScript for automation scripting.
Strong understanding of software testing principles, test case design, and defect lifecycle management.
Experience building or maintaining test automation frameworks (e.g., PyTest, Cypress, Selenium, Playwright).
Working knowledge of SQL and structured data validation.
Familiarity with ML/LLM fundamentals and evaluating model outputs.
Experience testing RESTful APIs and using tools such as Postman or pytest-requests.
Exposure to cloud environments (AWS, GCP, or Azure) and CI/CD pipelines (e.g., GitHub Actions, Jenkins).
Preferred Attributes
Curiosity to learn emerging AI technologies and apply traditional QA methods to new paradigms.
Analytical mindset with strong systems thinking — able to understand and test full workflows.
Ability to visualize and communicate complex test scenarios.
Comfort working in dynamic, iterative environments where experimentation drives improvement.
Align Technology is an equal opportunity employer. We are committed to providing equal employment opportunities in all our practices, without regard to race, color, religion, sex, national origin, ancestry, marital status, protected veteran status, age, disability, sexual orientation, gender identity or expression, or any other legally protected category. Applicants must be legally authorized to work in the country for which they are applying, and employment eligibility will be verified as a condition of hire.
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