Sr. / Staff ML Engineer, FM Training Integration - ML Compute
Apple - Santa Clara, United States of America
Posted May 13, 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
Sr. / Staff ML Engineer, FM Training Integration - ML Compute Santa Clara, United States of America We are looking for a ML Engineer to join our ML Compute team to help improve the efficiency, scalability, and reliability of model training and inference workloads in the cloud. In this role, you will lead the integration of large-scale ML workloads with cloud infrastructure, working cross-functionally with ML engineers, infrastructure engineers, and researchers to optimize performance, improve system efficiency, and drive high utilization of accelerator resources. We are a group of engineers to support training foundation models at Apple! We build infrastructure to support training foundation models with general capabilities such as understanding and generation of text, images, speech, videos, and other modalities and apply these models to Apple products. We are looking for engineers who are passionate about building systems that push the frontier of deep learning in terms of scaling, efficiency, and flexibility and delight millions of users in Apple products. Own the integration of large-scale model training workloads with accelerator-based cloud infrastructure, ensuring scalable and reliable execution. Drive performance optimization across the ML stack, including data pipelines, model execution, and distributed systems, to improve throughput, latency, and hardware utilization. Design and run benchmarks to evaluate model performance and infrastructure configurations, using results to guide optimization efforts. Build and improve tooling for observability, profiling, and debugging to increase visibility and reliability of ML workloads. Collaborate cross-functionally with ML engineers, infrastructure engineers, and researchers to improve system efficiency and scalability. Establish
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