Junior Android Engineer - Philadelphia, United States - Patterned Learning Ai

Description

Job DescriptionJunior Android Engineer - Remote Job, 1+ Year Experience

Annual Income:
$55K - $65K, OnsiteA valid work permit is necessary in the US/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:

Create and maintain best-in-class Android apps in KotlinExecute product specifications, and offer insight from the Android user's perspectiveEnsure Android and Software best practices are utilized in the code baseParticipate in spec reviews and offer solutions specific to your platformCollaborate with QA, Product, and Backend teamsParticipate in pull request meetings and general development meetingsRequirements:Experience implementing 3rd party SDKsComfortable with REST API integrationsExperience with git or similar source controlDesire to learn new technologies and remain on the cutting-edge1+ years experience in professional mobile development, ideally including experience in KotlinBS degree or equivalent work experienceSkills:Python DevelopmentWeb development (HTML, CSS, Angular)FastAPI, Keras, Flask, langchain, Pydantic, etcUI EngineerWindows Server ManagementStrong SQL Database experienceContent Management SystemsDatabases and Structured DataAWS experienceFlexible and adaptable with the ability to align to changing prioritiesAbility to work independentlyWhy 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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