Job description

We are looking for a Machine Learning Engineer to build the pipelines, infrastructure, and production systems that turn multimodal foundation models into shipping Apple Intelligence features. You will own end-to-end model delivery: building and scaling data curation and training pipelines, fine-tuning and optimizing large multimodal models for on-device and hybrid execution, standing up reproducible evaluation and regression testing for text and visual understanding, and hardening promising approaches into robust, maintainable production systems under real latency, memory, power, and privacy constraints. You will work closely with modeling, platform, hardware, and product engineering teams across Apple — taking future hardware design and product needs into account as you make implementation decisions — and you will have the opportunity to collaborate broadly to deliver the best possible products. Experience in deep learning with demonstrated work in at least one area of multimodal systems (e.g., vision, language, video, audio, etc.) Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX Experience with rapid prototyping, reproduction, and validation of research ideas Ability to work in a collaborative environment Ability to communicate the results of analyses in a clear and effective manner BS and a minimum of 3 years of relevant industry experience Master's or PhD, or equivalent practical experience, in Computer Science, Computer Vision, Machine Learning, or related technical field Deep expertise in multimodal foundation models, with a focus on practical applications Track record of translating research into practical applications either through published work or industry experience Strong applied research experience in at least one major area of model development (data curation, pre-training, fine-tuning, alignment, or evaluation), particularly as it applies to multimodal systems Experience with large-scale training pipelines, including working with large datasets and scaling models across distributed systems Experience bridging research ideas with production constraints

Requirements

Must-haves

Experience in deep learning with demonstrated work in at least one area of multimodal systems (e.g., vision, language, video, audio, etc.)
Proficiency in Python and in a modern deep learning framework such as PyTorch or JAX
Experience with rapid prototyping, reproduction, and validation of research ideas
Ability to work in a collaborative environment
Ability to communicate the results of analyses in a clear and effective manner
BS and a minimum of 3 years of relevant industry experience
Master's or PhD, or equivalent practical experience, in Computer Science, Computer Vision, Machine Learning, or related technical field
Deep expertise in multimodal foundation models, with a focus on practical applications
Track record of translating research into practical applications either through published work or industry experience
Strong applied research experience in at least one major area of model development (data curation, pre-training, fine-tuning, alignment, or evaluation), particularly as it applies to multimodal systems
Experience with large-scale training pipelines, including working with large datasets and scaling models across distributed systems
Experience bridging research ideas with production constraints

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningpython

Platform: apple
Effort: medium

Benefits

Benefits detected in the listing.

Vision