Senior Machine Learning Engineer
Apple
Listing collected from Apple's career site. NextChapter is independent of Apple.
Cupertino, CA · Posted November 10, 2026
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Pay compared with the market
This listing does not state pay. Across 106 open senior machine learning engineer listings that state pay, the middle half runs $200,000 to $238,000 and the median is $211,500.
Listed pay is what employers advertise, not what people are paid.
Senior Machine Learning Engineer salaries by state · Check your own pay
How AI is used in this kind of work
Among computer occupations, all other, AI is already used for a large share of the tasks. Measured exposure for computer occupations, all other: 31% (high).
Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.
Apple has filed 4 layoff notices in the last twelve months, covering 225 workers, most recently August 2026. These are public filings with state labor departments, and an open role can sit alongside them. See the filings
Skills this job asks for
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About the job
The ACAI Training & Inference Platform team is seeking a Senior / Staff Software Engineer with strong distributed systems expertise and deep experience in AI training and inference. As part of Apple Cloud AI (ACAI) within Apple’s Data Platform (ADP), you will design and build core capabilities for large-scale model training, post-training, fine-tuning, reinforcement learning, and high-performance inference. You will develop scalable AI execution frameworks, developer-facing APIs, and runtime optimizations that make it easier to train, adapt, and serve advanced models efficiently across Apple’s AI ecosystem. You will partner closely with AI researchers and engineers to translate evolving model architectures and workload requirements into reliable, high-performance training and inference solutions. This role is ideal for an engineer who combines deep distributed-systems expertise with hands-on knowledge of modern LLM training, post-training, and inference optimization.