Join Apple's Wireless Technologies and Ecosystems organization and drive innovation that matters As part of the Cellular, Connectivity and Satellite team, you'll build solutions for cutting-edge wireless technologies. As a Software Engineering Intern, you'll dive into an innovative project to revolutionize how we analyze logs. You will contribute to building an AI-powered system using an agentic workflow to enable intelligent system monitoring and recovery and seamlessly integrate and deploy the system on embedded devices. This project aims to significantly enhance system stability and accelerate insights from complex data. We seek proactive, collaborative individuals ready to make something extraordinary in a fast-paced environment. At Apple, dynamic, inspiring people and industry-defining technologies are the norm.
Description
This project builds on proven success of offline system behavioural analysis, where pre-trained AI/ML models successfully predicted anomalous system behaviour. In this project we plan to deploy previously studied AI/ML techniques and algorithms on embedded devices. These models will run in real-time, continuously monitoring and analysing system behaviour to anticipate possible anomalies and automatically trigger suitable recovery actions to ensure system stability.
Responsibilities
* Development of:
* Real-time on-device inference engine for anomaly detection
* Autonomous policy engine for executing recovery actions
* A feedback loop for model refinement
* Additionally:
Benchmark data demonstrating improvements in system stability
Minimum Qualifications
* Currently pursuing Master/PhD in Computer Science, Artificial Intelligence, Data Science, or similar.
* Proven software engineering skills in complex, multi-language systems, ideally proficient in Python and C.
* Proven expertise in machine learning with a passion for data-centric ML.
Preferred Qualifications
* Proven expertise in anomaly detection using AIML algorithms
* Proficiency in Python and C, ideally embedded C
* Basic Embedded SW and Real-Time OS (RTOS) concepts
* Familiarity with Large Language Model (LLM)
Demonstrated problem-solving aptitude and strong learning agility, with a proactive mindset and eagerness to quickly adapt to new technologies and concepts.
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