Machine Learning Engineer - Multimodal Intelligence

Apple • Seattle, WA

23h

About The Position

Join Apple's Multimodal Intelligence team to build and ship the Computer Vision and Machine Learning systems behind Apple Intelligence. This team focuses on data collection and curation, training and fine-tuning, evaluation, optimization, and on-device deployment of systems that combine Apple's sensing hardware with large foundation models. The goal is to create experiences where devices can understand the world around them privately, responsively, and on-device. As a Machine Learning Engineer, you will be responsible for building the pipelines, infrastructure, and production systems to deliver multimodal foundation models as shipping Apple Intelligence features. This includes owning end-to-end model delivery, scaling data curation and training pipelines, fine-tuning and optimizing models for on-device and hybrid execution, establishing reproducible evaluation and regression testing, and hardening approaches into robust production systems under strict constraints (latency, memory, power, privacy). You will collaborate with various engineering teams across Apple, considering future hardware and product needs in your implementation decisions.

Requirements

• 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.

Responsibilities

• Build and scale data curation and training pipelines.
• Fine-tune and optimize large multimodal models for on-device and hybrid execution.
• Stand up reproducible evaluation and regression testing for text and visual understanding.
• Harden promising approaches into robust, maintainable production systems under real latency, memory, power, and privacy constraints.
• Collaborate with modeling, platform, hardware, and product engineering teams.
• Take future hardware design and product needs into account when making implementation decisions.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level