Machine Learning Research Engineer - Embedded Wireless Systems

Apple is where wireless innovation transcends the ordinary, creating connections that feel like magic. Every time someone effortlessly switches their AirPods between devices, experiences crystal-clear audio in a crowded room, or enjoys seamless connectivity that just works, they're experiencing the result of our relentless pursuit of wireless perfection. In this role, you'll architect the invisible threads that connect our users to their digital lives. You'll go beyond industry standards, crafting embedded Bluetooth solutions that redefine what's possible. At Apple, wireless is creating experiences so intuitive that the technology disappears, leaving only wonder.

You'll work at the intersection of hardware and software, where every microsecond of latency matters and every milliwatt of power is precious!

Description
We are seeking a Machine Learning expert to join our wireless FW software group and explore innovative applications of ML algorithms in embedded wireless systems and protocols. This role focuses on cutting-edge research to integrate machine learning into wireless firmware architectures as well as protocol design, validation, and performance optimization. The position emphasizes experimental research, algorithm development, and proof-of-concept implementations in the wireless FW domain. This role offers the opportunity to develop novel solutions that bridge the gap between theoretical machine learning capabilities and practical wireless system implementations.","responsibilities":"Research & Development: Design, develop, and evaluate machine learning algorithms optimized for real-time wireless communication systems operating under strict real-time, power and computational constraints
Protocol Innovation: Create novel wireless protocols that intelligently incorporate ML techniques for enhanced performance, efficiency, and adaptability
Algorithm Optimization: Adapt existing wireless algorithms (e.g. Link Quality, QoS, Scheduling etc) by integrating ML approaches while maintaining real-time performance requirements
Data Collection & Analysis: Gather, analyze, and curate statistics from existing wireless devices and networks to enable ML model training and validation. Design and implement data collection frameworks for various wireless environments and use cases
System Integration: Develop lightweight ML models suitable for deployment on resource-constrained wireless devices, including edge computing scenarios
Performance Analysis: Conduct comprehensive performance evaluations through simulation, theoretical analysis, and experimental validation
Research Publication: Publish findings in top-tier conferences and journals, and present research at industry conferences
Collaboration: Work closely with hardware engineers, system architects, and product teams to ensure research translates into practical solutions
Technology Scouting: Stay current with emerging trends in ML, wireless communications, and edge computing technologies

Preferred Qualifications
Ph.D. in related field

Minimum Qualifications
Minimum: Master's degree (MSc) in one of the following fields: Computer Science or Mathematics with specialization in Machine Learning, Signal Processing and Telecommunications Engineering
Real-time Systems: Experience in real-time computing constraints and low-latency system design
Machine Learning: Deep understanding of ML algorithms, particularly those suitable for real-time applications (online learning, federated learning, reinforcement learning, neural networks)
Wireless Communications: Strong foundation in wireless communication principles, protocols
Programming: Proficiency in Embedded C, Python, MATLAB, C++, and ML frameworks
Optimization: Experience with convex optimization, resource allocation algorithms, and constraint satisfaction problems
Research Experience: Demonstrated track record of independent research through publications, patents, or significant project contributions
Knowledge of edge computing and distributed ML systems is advantageous
Personal Attributes: Self-Motivated: Ability to work independently, set research priorities, and drive projects from conception to completion
Analytical Thinking: Strong problem-solving skills with ability to tackle complex, multi-disciplinary challenges
Innovation-Oriented: Creative approach to research with ability to think outside conventional boundaries
Communication Skills: Excellent written and verbal communication skills for technical documentation and presentations
Collaborative Spirit: Ability to work effectively in cross-functional teams while maintaining independent research focus","internalDetails":null,"eeoContent":null