Job Description

We are seeking a detail-oriented and technically skilled Machine Learning QA Engineer to ensure the quality, accuracy, and reliability of our ML models and systems. You will work at the intersection of quality assurance and machine learning, developing automated and manual test strategies, validating data pipelines, and verifying ML model performance against business objectives.
Key Responsibilities:
Design and implement test strategies for machine learning pipelines, APIs, and models.
Develop automated testing frameworks for validating ML model inputs, outputs, and performance.
Collaborate with ML engineers, data scientists, and DevOps teams to ensure seamless model deployment.
Evaluate data quality and integrity throughout the ML lifecycle.
Test for accuracy, bias, fairness, reproducibility, and drift in models.
Validate and monitor ML models in production environments.
Write detailed test plans, test cases, and quality documentation.
Participate in code reviews and contribute to QA best practices in ML workflows.
Requirements:
Bachelors/Masters degree in Computer Science, Data Science, Engineering, or related field.
Proven experience in QA/testing of machine learning systems or data products.
Strong understanding of software testing practices (unit, integration, system, regression).
Experience with Python and testing frameworks (e.g., PyTest, unittest).
Familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Understanding of data validation tools (e.g., Great Expectations, TensorFlow Data Validation).
Experience testing RESTful APIs and backend systems.
Familiarity with CI/CD pipelines and version control systems (e.g., Git, Jenkins).
Preferred Qualifications:
Experience with MLOps tools like MLflow, Airflow, or Kubeflow.
Exposure to cloud platforms (AWS/GCP/Azure) for model deployment and monitoring.
Knowledge of model interpretability, explainability, and ethical AI principles.
Experience working in Agile/Scrum teams.

Skills Required