Senior Machine Learning Engineer, Video Quality Systems
Apple - Cupertino, United States of America
Posted Apr 22, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Not verified last checked Jun 13, 2026
- Salary
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- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.
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Schedule
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- Weekend work
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Application
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Senior Machine Learning Engineer, Video Quality Systems Cupertino, United States of America 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. 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
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