About the job
As a Machine Learning Engineer in the Camera Hardware Engineering group you will be responsible for all research, design, development, test, and qualification of camera hardware for Apple products. This team is seeking an experienced Machine Learning Engineer with a background in Camera and image sensor technologies. You will bring your expertise to the team and be responsible for ongoing design, evaluation, benchmarking and characterization of Apple camera products.
Responsibilities
ML Algorithms: Research, design, and implement machine learning algorithms for various camera tasks, including image and video processing, computational photography, and scene understanding.\nData-Driven Development: Collaborate with data collection teams to curate large-scale image and video datasets and perform data analysis and preprocessing to train and validate your ML models effectively.\nPrototyping and Evaluation: Develop prototypes of ML algorithms and evaluate their performance through rigorous testing and user feedback. Your insights will drive continuous improvement.
Qualifications
Minimum
Bachelor’s degree in Computer Science, Electrical Engineering, Physics, Optics, or a related field\nExperience with Python programming and deep learning frameworks like PyTorch\nExperience with machine learning and computer vision principles, and algorithms.\nExperience with camera and/or image sensor technologies.
Preferred
MS or Ph.D. in Machine Learning, EE, CS, Physics, Optics, or equivalent and 3+ years of experience in machine learning research or relevant industry experience\nExperience with applying deep learning to various computer vision tasks, such as: Object recognition, Image segmentation, Inpainting, and Anomaly detection\nExperience in applying generative AI and reinforcement learning techniques to enhance hardware design processes. \nExperience with using advanced ML methods to optimize system architectures and improve image quality in the hardware design domain.\nExperience with image quality metrics and evaluation methodology\nExperience with camera components, functions, and the camera ISP pipeline\nExperience with megapixel CMOS image sensor technology, lens selection, and qualification