Senior Machine Learning Engineer, Video Quality Systems

Apple's Camera ISP Algorithm team is looking for dedicated engineers to shape the future of photography and video across all Apple products. You'll work on powerful camera technology, image signal processing, and machine learning, literally defining what makes an Apple camera better. As part of the Camera ISP Algorithm team, you'll have real creative freedom to innovate and iterate quickly, interacting directly with silicon design, camera HW/SW, and QA teams. If you're a self-starter who wants to see your ideas go from concept to product, this is your chance to make an impact on how people capture life's most meaningful moments!

As a Senior Machine Learning Engineer, you will tackle one of the most persistent challenges in video technology: reliably measuring perceived visual quality at scale. While human expert evaluation remains the gold standard for accuracy, it is resource-intensive and slow. Conversely, traditional automated metrics offer speed, but often fail to correlate meaningfully with human perception.\n\nYou will be an expert in designing a hybrid evaluation framework. By leveraging large-scale outsourced subjective data, you will characterize the boundaries of existing automated metrics and inject domain and \"world knowledge\" to apply them only where they are statistically reliable. Ultimately, your goal will be to design and tune novel, explainable metrics. We are explicitly looking for an approach grounded in first principles of signal processing and human vision, rather than relying on opaque, \"black-box\" machine learning models that simply output a quality score. Your work will directly accelerate our core engineering efforts by providing developers with rapid, trustworthy, and actionable feedback.

MS in Machine Learning, Computer Science, Applied Mathematics, or a related discipline and minimum 10 years relevant industry experience. \nDemonstrated experience on Image/Video Quality Assessment (IQA/VQA), image processing, or computational vision.\nTrack record in statistical analysis, correlation methodologies, and data modeling.\nProficiency in algorithm architecture design and implementation.

PhD in Machine Learning, Computer Science, Applied Mathematics, or a related discipline.\nExperience managing or scaling outsourced/crowdsourced subjective evaluation campaigns (e.g., using ITU-T standards).\nTrack record of developing explainable, non-black-box algorithms for image or video analysis.\nProven experience designing, conducting, and analyzing psycho-physical or psycho-visual experiments for subjective quality evaluation.\nDemonstrated knowledge of the human visual system (HVS), perceptual artifacts, and traditional signal processing, evidenced through publications, coursework, or applied project work.\nWorking knowledge with modern video processing pipelines, compression standards, and enhancement algorithms.\nStrong publication record in relevant venues (e.g., VQEG, ICIP, HVEI, SPIE) or equivalent industry patents.\nAbility to translate complex perceptual phenomena into clear, actionable engineering requirements, as demonstrated through technical writing, presentations, or cross-functional collaboration.