AI QA Engineer at TeamViewer GmbH

Level: Experienced

Job Field: Data, Test/QA

Employment Type: Full Time

Contract Type: Permanent employment

Salary: from 3.843 € Gross/Month

Location: Linz

Working Model: Hybrid, Onsite

Job Summary

In this role, you will develop automated tests for AI/ML models, integrate them into CI/CD pipelines, and monitor regression tests to ensure the quality of AI applications.

Job Technologies

Your role in the team

Design, implement, and maintain automated tests, especially for systems using AI/ML models.
Integrate tests into CI/CD pipelines, monitor regression runs, and adjust strategies for AI components.
Create and maintain comprehensive test plans covering requirements, use cases, and AI-specific quality aspects (performance, accuracy, robustness, fairness).
Actively contribute to scrum ceremonies, providing feedback from both a user and AI quality perspective.
Collaborate with Product Owners, Tech Leads, Developers, and AI Engineers from use case definition to model refinement to ensure testability.
Assess datasets for quality and bias and evaluate their impact on model behavior.
Apply foundational knowledge of machine learning and statistics to design meaningful tests and interpret AI results.
Drive continuous improvement in QA processes, especially AI-related testing methods.
Work closely with QA colleagues and development teams across units to align on quality standards and address AI-specific challenges.

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Our expectations of you

Qualifications

Starkes Verständnis von QA-Prinzipien, SDLC und grundlegenden maschinellen Lernkonzepten.
Ability to design meaningful AI/ML test scenarios, focusing on model behavior, edge cases, and reliability.
Knowledge of evaluation metrics and basic statistical methods (precision, recall, F1, AUC, distributions, sampling).
Familiarity with AI evaluation tools (e.g., MLflow, Weights & Biases) and standard automation or CI/CD tools (e.g., Selenium, GitLab, Jenkins).
Awareness of ethical AI principles and best practices for responsible AI.
Good communication skills to collaborate with different stakeholders.

Experience

Experience in test automation, software development, or quality assurance, with proficiency in Python or similar languages.
Experience assessing datasets, including labeling, data quality checks, and identifying bias, data drift, or concept drift.

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