This is a remote position.
Junior Test Engineer - Remote Job, 1+ Year Experience
Annual Income: $60K - $75K
About us: Patterned Learning is a platform that aims to help developers code faster and more efficiently. It offers features such as collaborative coding, real-time multiplayer editing, and the ability to build, test, and deploy directly from the browser. The platform also provides tightly integrated code generation, editing, and output capabilities.
Responsibilities:
Collaborate with the development team to understand project requirements and develop comprehensive test plans
Execute manual and automated tests to identify software defects and ensure the overall quality of the product
Document test cases, test scenarios, and test results to maintain a comprehensive and up-to-date testing repository
Identify, report, and track software bugs using issue-tracking systems, ensuring their timely resolution
Perform regression testing to ensure previously fixed issues do not resurface
Collaborate with cross-functional teams to drive continuous improvement in the testing process
Stay up-to-date with the latest industry trends and technologies in software testing methodologies
Qualifications:
Bachelor's degree in Computer Science, Software Engineering, or a related field
Strong knowledge of software testing principles, methodologies, and tools
Familiarity with manual and automated testing techniques and tools, such as Selenium, JUnit, or similar
Basic understanding of programming languages (Java, Python, C++, etc.) to analyze code and develop test scripts
Excellent problem-solving skills and attention to detail
Strong communication and collaboration skills, with the ability to work effectively in a team environment
Ability to work independently and manage time efficiently to meet project deadlines
Experience with Agile/Scrum development methodologies is a plus
Why Patterned Learning LLC?
Patterned Learning can provide intelligent suggestions, automate repetitive tasks, and assist developers in writing code more effectively. This can help reduce coding errors, improve productivity, and accelerate the development process.
The pattern recognition is particularly relevant in the context of coding. Neural networks, especially deep learning models, are commonly employed for pattern detection and classification tasks. These models simulate human decision-making and can identify patterns in data, making them well-suited for tasks like code analysis and generation.
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