Software QA & Automation Engineer
About The Position
NeuReality's software department is looking for an experienced and highly motivated Software QA & Automation 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[1]effective.
Responsibilities:
The Software QA & Automation Engineer is responsible for all aspects of software testing and release:
• 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 (Python, Java or Javascript).
- 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
- Proficiency in test automation tools and frameworks (e.g., Selenium, PyTest, TestNG, JUnit).
- Familiarity with version control systems (e.g., Git) and CI/CD pipelines (e.g., Jenkins, GitLab, or CircleCI).
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:
AI/ML Knowledge
- 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.
- Computer vision, image, or audio processing knowledge
- Experience working in AI company.
- Experience with testing Cloud/data center applications