Sr. / Staff ML Engineer, FM Training Integration - ML Compute

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.\\n

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.

5+ years of experience in software engineering, ML infrastructure, or related domains.\n\nHands-on experience with machine learning workflows, including training, evaluation, and inference at scale.\n\nProficiency in Python and experience with at least one major ML framework (e.g., PyTorch or JAX).\n\nExperience with cloud-based infrastructure and distributed systems (e.g., containers, orchestration, storage, and networking).\n\nBachelor's degree in Computer Science, Engineering, or a related field.\n

Experience working with accelerator-based systems (e.g., GPTPUs), including performance tuning and debugging of ML workloads.\n\nHands-on experience with distributed training or inference at scale (e.g., data, model, or pipeline parallelism).\n\nExperience optimizing large-scale ML systems, including bottleneck analysis across compute, memory, and networking.\n\nFamiliarity with profiling, tracing, and benchmarking tools for ML workloads (e.g., PyTorch Profiler, NVIDIA Nsight).\n\nExperience building or operating ML infrastructure using containerization and orchestration frameworks (e.g., Docker, Kubernetes).\n\nAdvanced degree in Computer Science, Engineering, or a related field.\n