Junior Full Stack Developer React/Python

This is a remote position.

Junior Full Stack Developer React/Python  - Remote Job, 1+ Year Experience

Annual Income: $62K - $71K

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:

• Architect, design, and implement solutions to complex engineering problems
  

• Provide coding expertise in Python (FastAPI and Django), React JS, TypeScript, SQL, NoSQL, and AWS to develop frontend and backend applications.
  

• Collaborate with other developers to deliver working software solutions
  

• Assist project management with planning, product roadmap planning, and release planning
  

• Assist with code reviews and technical reviews
  

Qualifications:

• 1 year of professional experience with React JS and Python
  

• Bachelor's degree in computer science, engineering, or related
  

• Good communication skills, both oral and written
  

Nice to Have experience:

• Mobile app development in React Native
  

• Thirst For Tech Learning
  

Benefits

• 401(k) matching
  

• Flexible spending account
  

• Flextime
  

• Health insurance
  

• Health savings account
  

• Paid time off
  

• Relocation assistance
  

• Tuition reimbursement
  

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.