Senior Machine Learning Engineer, Video Quality Systems

Apple • Cupertino, CA

1h

About The Position

Apple’s Camera ISP Algorithm team is seeking dedicated engineers to advance photography and video technology across all Apple products. This role involves working with powerful camera technology, image signal processing, and machine learning to enhance Apple camera performance. As part of the Camera ISP Algorithm team, the engineer will have creative freedom to innovate and iterate quickly, collaborating with silicon design, camera HW/SW, and QA teams. The position offers an opportunity to impact how people capture life’s moments, taking ideas from concept to product. As a Senior Machine Learning Engineer, the primary challenge will be to reliably measure perceived visual quality in video at scale. The role involves designing a hybrid evaluation framework that leverages large-scale outsourced subjective data to characterize existing automated metrics and apply them reliably. The ultimate goal is to design and tune novel, explainable metrics grounded in signal processing and human vision, avoiding opaque "black-box" machine learning models. This work will provide rapid, trustworthy, and actionable feedback to developers, accelerating core engineering efforts.

Requirements

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

Nice To Haves

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

Responsibilities

• Tackle the challenge of reliably measuring perceived visual quality at scale in video technology.
• Design a hybrid evaluation framework for visual quality.
• Leverage large-scale outsourced subjective data to characterize the boundaries of existing automated metrics.
• Inject domain and "world knowledge" to apply automated metrics only where they are statistically reliable.
• Design and tune novel, explainable metrics grounded in first principles of signal processing and human vision.
• Accelerate core engineering efforts by providing developers with rapid, trustworthy, and actionable feedback.

What This Job Offers

• Career Level: Senior
• Number of Employees: 5,001-10,000 employees