Junior Web development programmer - #1692712

Patterned Learning Career


Date: 3 weeks ago
City: Albuquerque, NM
Salary: $60,000 - $65,000 per year
Contract type: Full time
Remote
- Remote Job, 1+ Year Experience

Annual Income: $60K - $65K, Onsite

A valid work permit is necessary in the US/Canada

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.

Description

This position will provide the opportunity to work closely with project managers, graphic designers, and programmers to create innovative online solutions. You will help with the general maintenance of company websites, manage multiple tasks, and meet deadlines promptly.

Requirements:

  • Well-versed in Internet technologies and applications
  • A working knowledge of HTML, CSS, Javascript, PHP, JSP, ASP, etc.
  • Experience with server-side technologies (i.e. ASP, PHP, .NET, etc.) and interfacing with a database (i.e. MySQL, SQL Server, Access)
  • A team-oriented, hard-working individual who likes to collaborate with others
  • Avid internet user with strong computer skills
  • Experience with back-end web development
  • Strong design skills with a keen attention to detail
  • Social media and blogging experience preferred

Details:

  • Flexible hours (must be able to work a minimum of 30 hours per week)
  • Can work from home or office
  • Casual work environment

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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