Junior Android Engineer

Junior Android Engineer - onsite Job, 1+ Year ExperienceAnnual Income: $61K - $76KA valid work permit is necessary in the USAbout 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:Create and maintain best-in-class Android apps in Kotlin Execute product specifications, and offer insight from the Android user's perspective Ensure Android and Software best practices are utilized in the code base Participate in spec reviews and offer solutions specific to your platform Collaborate with QA, Product, and Backend teams Participate in pull request meetings and general development meetings Requirements:Experience implementing 3rd party SDKs Comfortable with REST API integrations Experience with git or similar source control Desire to learn new technologies and remain on the cutting-edge 1+ years experience in professional mobile development, ideally including experience in Kotlin BS degree or equivalent work experience Skills:Python Development Web development (HTML, CSS, Angular) FastAPI, Keras, Flask, langchain, Pydantic, etc UI Engineer Windows Server Management Strong SQL Database experience Content Management Systems Databases and Structured Data AWS experience Flexible and adaptable with the ability to align to changing priorities Ability to work independently 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.