Overview
Junior Full Stack Developer React/Python Jobs in Toronto, Ontario at Patterned Learning Career
Title: Junior Full Stack Developer React/Python
Company: Patterned Learning Career
Location: Toronto, Ontario
This is a remote position.
Junior Full Stack Developer React/Python – Remote Job, 1+ Year ExperienceAnnual Income:$62K – $72K
A valid work permit is necessary in CanadaAbout 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.
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.
