Infrastructure and Automation Test Engineer

NeuReality's software department is looking for an experienced and highly motivated Infrastructure and Automation Test Engineer to join us and be part of NeuReality’s next-generation, state-of-the-art AI inference server development.

The group designs, develops, validates, and releases inference server and programming SDK software products to make AI deployment easy and cost/power-effective.

Responsibilities:

• QA activities that are required for releasing high-quality products to NeuReality's customers.
• Tight collaboration with architects and development teams.
• Reviewing specifications and technical design documents to provide timely and meaningful feedback.
• Creating product test plans and managing their execution.
• Defining metrics for quality evaluation.
• Design and development of new automatic testing approaches for various features and products developed by NeuReality.
• Consistently reviewing, analyzing, and improving test automation infrastructure and reports.

Requirements:

• BSc in Computer Engineering / Computer Science / Electrical Engineering
• 5+ years’ experience in developing automation/validation products with Python as a leading language.
• Experience in infrastructure development (e.g., test environments, test execution frameworks, reporting tools).
• Experience in validation of complex systems and performance tests.
• Formal and practical knowledge of testing methodologies.
• Hands-on expertise in test writing and automation.
• Excellent knowledge of Linux operating system and good understanding of networking.
• Level of exposure to cloud capabilities, including Kubernetes, virtualization, Docker, etc.
• Proficiency in test automation tools and frameworks (e.g., PyTest, TestNG, JUnit).
• Familiarity with version control systems (e.g., Git) and CI/CD pipelines (e.g., Jenkins, GitHub).

Problem-Solving and Analytical Skills

• Ability to identify, analyze, and debug issues within complex systems, including AI pipelines.
• Knowledge of data validation and techniques to test AI fairness, bias, and performance.
• Communication and Teamwork
• Strong verbal and written communication skills for collaborating with technical and non-technical stakeholders.

Advantages:

• Basic understanding of AI/ML concepts such as training, inference, data preprocessing, and model evaluation.
• Experience testing AI models and ensuring their reliability under diverse data inputs is a plus.
• Knowledge of computer vision, image, or audio processing.
• Experience working in AI-focused companies.
• Experience with testing cloud/data center applications.