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! Description 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. You 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. Responsibilities: Subjective Testing & Analysis: Design, oversee, and analyze large-scale psycho-visual experiments to collect high-quality subjective video evaluation data. Metric Characterization: Evaluate existing objective Video Quality Assessment (VQA) metrics against human baselines to determine their correlation and operational limits. Context-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 artifacts. First-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 actionable. Cross-Functional Collaboration: Partner with algorithmic development teams to integrate your evaluation frameworks into fast, automated feedback loops that guide the engineering process. Preferred Qualifications 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. Minimum Qualifications MS in Machine Learning, Computer Science, Applied Mathematics, or a related discipline and 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. Pay & Benefits At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location. Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.