Junior QA Assurance Analyst

This is a remote position.

Junior QA Assurance Analyst - Remote Job, 1+ Year Experience

Annual Income: $56K - $75K

A valid work permit is necessary in the US

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 cross-functional teams to understand software requirements and specifications.
• Participate in the creation and execution of test plans, test cases, and test scripts.
• Identify, document, and track software defects and issues using bug-tracking tools.
• Conduct thorough regression testing to verify fixes and ensure software quality.
• Assist in the development of automated test scripts to improve efficiency.
• Perform exploratory testing to uncover potential issues and usability concerns.
• Work closely with developers to communicate issues and validate bug fixes.
• Provide timely and detailed reports on testing progress and results.
• Contribute to the continuous improvement of QA processes and methodologies.
  
  

Qualifications:

• Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience).
• Strong passion for software quality assurance and a desire to learn and grow.
• Familiarity with software testing concepts, methodologies, and best practices.
• Basic understanding of programming languages and scripting (e.g., Java, Python).
• Excellent analytical and problem-solving skills.
• Detail-oriented with a keen eye for identifying issues and inconsistencies.
• Effective communication skills and ability to work collaboratively in a team environment.
• Experience with testing tools and bug-tracking systems 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.

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.