Senior Machine Learning Engineer, Video Quality Systems

Apples Camera ISP Algorithm team is looking for dedicated engineers to shape the future of photography and video across all Apple products. Youll 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 youll have real creative freedom to innovate and iterate quickly interacting directly with silicon design camera HW/SW and QA teams. If youre 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 lifes 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 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.

Subjective Testing u0026 Analysis: Design oversee and analyze large-scale psycho-visual experiments to collect high-quality subjective video evaluation Characterization: Evaluate existing objective Video Quality Assessment (VQA) metrics against human baselines to determine their correlation and operational -Aware Evaluation: Develop methodologies to classify video content and apply world knowledge identifying exactly which automated metrics succeed or fail on specific types of content and -Principles Design: Design tune and validate new objective quality metrics based on the human visual system (HVS) and mathematical first principles ensuring the resulting scores are highly explainable and -Functional Collaboration: Partner with algorithmic development teams to integrate your evaluation frameworks into fast automated feedback loops that guide the engineering process.

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 record in statistical analysis correlation methodologies and data in algorithm architecture design and implementation.

PhD in Machine Learning Computer Science Applied Mathematics or a related 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 experience designing conducting and analyzing psycho-physical or psycho-visual experiments for subjective quality knowledge of the human visual system (HVS) perceptual artifacts and traditional signal processing evidenced through publications coursework or applied project knowledge with modern video processing pipelines compression standards and enhancement publication record in relevant venues (e.g. VQEG ICIP HVEI SPIE) or equivalent industry to translate complex perceptual phenomena into clear actionable engineering requirements as demonstrated through technical writing presentations or cross-functional collaboration.