Senior Machine Learning Engineer, Video Quality Systems at Apple

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

22 Jul, 26

Salary

0.0

Posted On

23 Apr, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Image Signal Processing, Video Quality Assessment, Statistical Analysis, Algorithm Architecture, Data Modeling, Signal Processing, Human Visual System, Computational Vision, Python, Video Compression, Psycho-physical Experimentation, Technical Writing, Cross-functional Collaboration

Industry

Computers and Electronics Manufacturing

Description

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. 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. 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.

Responsibilities

You will design and tune novel, explainable metrics for video quality assessment by leveraging large-scale subjective data. Your work will provide developers with rapid, trustworthy, and actionable feedback to improve camera technology.