Senior Machine Learning Research Engineer, AIML

Summary

Are you passionate about Generative AI and excited to work on groundbreaking modeling technologies that will enrich the lives of billions? The AIML team within Apple's software organization is a multidisciplinary group operating at the intersection of Multimodal Foundation Models, Efficient and Scalable ML Infrastructure, and Personalized Intelligent Experiences. As a senior machine learning engineer on our team, you will design software systems and algorithms that enable performant, scalable training and inference for Apple's AI-driven experiences across both on-device and server environments. This role also includes opportunities to open source your work. Join our team of highly skilled, impact-focused engineers!

Description

We're seeking strong senior machine learning research engineers to help build next-generation tools for large-scale deep learning. You'll join a team focused on accelerating training and inference speed, improving scalability, and advancing Apple's centralized ML platform. Candidates should bring polished coding skills and a passion for machine learning and computational science. We offer a respectful work environment, flexible responsibilities, and access to world-class experts and growth opportunities.

In this role, you will develop core components for our scalable ML platform, push the limits of existing training technologies, and create new techniques to overcome system constraints. Your work will be deployed on high-impact tasks across teams building Apple Intelligence products, with opportunities to open-source your contributions.

We are especially looking for PyTorch-focused ML experts driving system-level efficiency from on-device to large-scale models. If you have deep experience with PyTorch internals and high-performance ML infrastructure, we'd love to hear from you. We encourage releasing contributions as open source.

Preferred Qualifications

Experience working on AI/ML-optimized runtime stacks.
Familiarity with parallelization algorithms for large model training.
Familiarity with recent developments in foundation model architectures.
Experience with parallel training libraries such as PyTorch Distributed (torch.distributed), DeepSpeed, or FairScale.
Experience building ML models for on-device inference.
Publication record at ML conferences such as MLSys, NeurIPS, etc.

Minimum Qualifications

PhD or Master's degree in Computer Science, or equivalent industry experience, with 3+ years of experience in the AI/ML field.
Strong Python programming skills.
Solid understanding of software–hardware co-design principles and algorithms.
Solid understanding of the PyTorch software stack and experience maintaining state-of-the-art ML frameworks.
Strong understanding of LLM architectures and their core building blocks.