Patterned Learning Career Looking for Full-Stack Engineer Entry Level at Albuquerque, NM Full Time

Full-Stack Engineer Entry Level – Remote Job, 1+ Year Experience

Annual Income: $55K – $60K

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.

What You Get To Do every day:

• Build and maintain business-critical software to process and record customers’ retirement savings
• Collaborate with other engineers and stakeholders to share knowledge and build expertise
• Develop ownership over domains in our system and make informed engineering tradeoffs
• Advocate for and delight internal and external users
• Improve engineering standards, tooling, and processes

What you bring to the role:

• 1 year of experience building and maintaining software in production
• Desire to work with the following technologies: Node, TypeScript, React, AWS and PostgreSQL
• Top-notch communication skills. You can communicate well with engineers and non-engineers alike
• Enjoy mentoring other engineers and deeply reviewing their code
• Proactive and empathetic mindset – you love to roll up your sleeves to fix problems for our customers

Nice to have:

• Experience using and developing component libraries/design systems
• Familiarity with React Hooks
• Prior knowledge of GraphQL
• System design & architecture experience
• Self-driven and willing to learn

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.