Job Description

Locations

• Seattle, Washington, United States 98117

Salary

Skills Required

• scaling large modelsintermediate
• optimizing large modelsintermediate
• integrating models and agentsintermediate
• collaborating with engineers and scientistsintermediate
• researching methods to improve alignmentintermediate
• researching methods to improve reasoningintermediate
• researching methods to improve adaptationintermediate
• ensuring efficiencyintermediate
• ensuring scalabilityintermediate
• ensuring privacyintermediate
• staying current with emerging researchintermediate
• identifying techniques for real-world deploymentintermediate
• designing large-scale data pipelinesintermediate
• robust training of foundation modelsintermediate
• fine-grained evaluation of foundation modelsintermediate
• working with massive multimodal datasetsintermediate
• enhancing reasoningintermediate
• enhancing multimodal understandingintermediate
• adapting models for ecosystemintermediate
• cloud-scale infrastructureintermediate
• on-device intelligenceintermediate
• partnering with cross-functional teamsintermediate
• seamless integration into productsintermediate
• creating intelligent natural user experiencesintermediate

Required Qualifications

• BS and a minimum of 3 years relevant industry experience. (experience, 3 years)
• Proficient programming skills in Python and experience with at least one modern deep learning framework (PyTorch, JAX, or TensorFlow). (experience)
• Experience working with large-scale training pipelines and distributed systems. (experience)

Preferred Qualifications

• PhD, or equivalent practical experience, in Computer Science, Machine Learning, or a related technical field. (experience)
• Demonstrated expertise in deep learning, with either: A publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV,COLM, etc), or a strong track record of applying deep learning techniques to real-world products. (experience)
• Experience with foundation models (language, vision-language, or multimodal). (experience)
• Familiarity with large-scale data pipelines, including data curation, preprocessing, and efficient storage. (experience)
• Experience fine-tuning or optimizing large models for production deployment. (experience)
• Knowledge of retrieval-augmented generation (RAG), personalization, or grounding techniques. (experience)
• Familiarity with privacy-preserving or on-device machine learning. (experience)
• Ability to work effectively in a cross-functional, collaborative environment. (experience)

Responsibilities

• You will work on advancing the capabilities of foundation models and steering them toward real-world applications in Apple products. This includes researching and developing methods to improve alignment, reasoning, and adaptation of large models to practical use cases, while ensuring they meet Apple’s standards for efficiency, scalability, and privacy. A key part of your role will be to stay current with emerging research and identify techniques suitable for real-world deployment, bridging the gap between state-of-the-art advancements and production-ready solutions.
• You will design and optimize large-scale data pipelines to enable robust training and fine-grained evaluation of foundation models, working with massive multimodal datasets to push the limits of performance. You will explore new ways to enhance reasoning and multimodal understanding, adapting models for Apple’s unique ecosystem, from cloud-scale infrastructure to on-device intelligence.
• Collaboration will be central to your work—you will partner with cross-functional teams of engineers and researchers bring these models to life, ensuring seamless integration into Apple products and creating intelligent natural user experiences.

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