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Apple Machine Learning Engineer China, Shanghai 609447027
14.04.2025
You will be responsible for developing defect detection and automatic optical inspection of Apple products, with a focus to develop/ deploy AIML models, and optimize model inference on edge devices (CPU/GPU/ANE)- Implement the full machine learning lifecycle—from data collection and preprocessing to model development, deployment, and monitoring.- Develop scalable ML software deployment pipelines across multiple projects and hardware platforms.- Stay ahead of AI/ML advancements and integrate the latest into our technology roadmap- Analyze large amounts of data to qualify and improve algorithm/ models.- Review and optimize algorithms developed internally and by third parties.
- MS/PhD in Computer Science, Electrical Engineering, Imaging Science.
- Strong expertise in image processing and computer vision, such as feature extraction and representation, image registration, segmentation, distortion correction, image restoration, frequency domain analysis, change detection, image quality assessment, and 3D computer vision.
- Hands-on experience in developing and deploying supervised and unsupervised machine learning models (e.g., CNN, GAN, AE, Transformer, Clustering, SVM) for defect detection, anomaly detection, image segmentation, classification, restoration.
- Proficiency in C++, MATLAB, Python, Git, and deep learning frameworks such as Keras, TensorFlow, and PyTorch.
- Strong written and verbal communication skills, with the ability to effectively collaborate across diverse teams.
- Experience with AIML models/ applications on edge devices.
- Experience with HW optimization / GPU acceleration.
- Knowledge of ML Dev Ops with scalable cloud platforms like AWS/GCP.
- Experience in iOS development (Objective C/ Swift ).
- Experience in SW modularization, scalability, unit/ regression testing.
- Experience in modeling and characterization for imaging system.
- A proactive attitude with the ability to explore uncharted technology spaces and deliver innovative solutions in a timely fashion.
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