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

Apple • Santa Clara, CA

8h

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

• Lead the integration of large-scale ML workloads with cloud infrastructure.
• Work cross-functionally with ML engineers, infrastructure engineers, and researchers to optimize performance.
• Improve system efficiency.
• Drive high utilization of accelerator resources.

What This Job Offers

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