Machine Learning Engineer, On-Device ML - Apple Ads
3 Months ago • All levels • $143,100 PA - $264,200 PA
Research Development
Job Description
The Machine Learning Engineer will design, develop, and optimize on-device machine learning models for learning user preferences within the App Store ecosystem. The engineer will implement, benchmark, and fine-tune deep learning models for efficiency, latency, and power consumption on iOS devices. They will work with Core ML, Metal, and other Apple ML frameworks to deploy models efficiently and securely. The role emphasizes on-device computation to ensure user privacy while developing personalized experiences using Private Federated Learning and Differential Privacy techniques. They will also optimize models for various iOS devices and develop tools for model compression, quantization, pruning, and hardware acceleration. This role requires staying current with advancements in privacy-preserving AI, on-device learning, and Apple ML.
Must Have:
- Design and develop on-device machine learning models.
- Implement and fine-tune deep learning models for iOS.
- Work with Core ML, Metal, and Apple ML frameworks.
- Ensure on-device ML computations for user privacy.
- Apply Federated Learning and Differential Privacy.
- Optimize models for various iOS devices.
- Develop tools for model compression and acceleration.
- Design, develop, and optimize on-device machine learning models for learning user preferences within the App Store ecosystem. - Implement, benchmark, and fine-tune deep learning models for efficiency, latency, and power consumption on iOS devices. - Work with Core ML, Metal, and other Apple ML frameworks to deploy models efficiently and securely. - Ensure all ML computations remain on-device, preserving user privacy while maintaining model effectiveness. - Develop and apply Private Federated Learning and Differential Privacy techniques to ensure user data never leaves the device while enabling personalized experiences. - Optimize models for various iOS devices, ensuring performance across different generations and hardware capabilities. - Develop tools and techniques for model compression, quantization, pruning, and hardware acceleration to enable real-time inference. - Stay updated with the latest developments in privacy-preserving AI, on-device learning, and Apple ML advancements.
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