Senior / Staff Machine Learning Engineer, Apple Cloud AI Platform

Apple • Seattle, WA

2d

About The Position

Apple Cloud AI is building scalable model training services and frameworks to accelerate development of real-world AI applications. Its platform supports the full model training lifecycle, from initial experiments through large-scale pre-training, fine-tuning, and reinforcement learning on thousands of GPUs and TPUs, with a focus on usability and accelerator efficiency.

The Apple Cloud AI Platform team works with Apple researchers and ML engineers to train and ship models to Apple's quality and privacy standards. In this role, you will apply hands-on large-scale model training expertise to important training efforts. You will develop recipes for pre-training, fine-tuning, reinforcement learning, and model distillation, and apply techniques such as parameter-efficient fine-tuning, preference optimization, and quantization-aware training. You will diagnose loss spikes and numerical instability, and select precision, parallelism, and optimizer settings that support reliable model convergence at scale on GPUs and TPUs. At the Staff level, you will help set technical direction for Apple's model training and lead efforts spanning multiple teams.

Requirements

• 8+ years of experience in large-scale model training, including reinforcement learning, from research to production.
• 5+ years of experience building and operating large-scale model training systems in production.
• Experience with common fine-tuning and distillation techniques, such as supervised fine-tuning, parameter-efficient fine-tuning with LoRA, and teacher-student distillation.
• Hands-on experience with at least one higher-level training framework, such as Megatron-LM, DeepSpeed, TorchTitan, NeMo, AXLearn, or MaxText.
• Deep expertise in PyTorch or JAX, including distributed training features.
• Strong programming skills in Python or C++.
• Strong collaboration and written and verbal communication skills, with a track record of leading technical work across teams.
• BS, MS, or PhD degree in Computer Science, Computer Engineering, or equivalent practical experience.

Nice To Haves

• Experience with low-precision training and the numerical issues it brings.
• Experience training large models with data, tensor, pipeline, or expert parallelism across hundreds to thousands of GPUs or TPUs.
• Strong understanding of GPU and TPU architecture, including memory hierarchy, matrix units, and high-bandwidth memory, and how they shape training performance.
• Experience with collective communication libraries such as NCCL and high-speed interconnects such as NVLink, InfiniBand, and TPU ICI.
• Experience profiling and optimizing training workloads with tools such as Nsight Systems, PyTorch Profiler, or XProf.
• Experience building reinforcement learning or post-training systems for large models.
• Open source contributions to training frameworks, or publications at top ML venues such as NeurIPS, ICML, or ICLR.

Responsibilities

• Develop training recipes for pre-training, fine-tuning, reinforcement learning, and model distillation.
• Apply techniques such as parameter-efficient fine-tuning, preference optimization, and quantization-aware training.
• Diagnose loss spikes and numerical instability in training.
• Choose precision, parallelism, and optimizer settings to enable reliable model convergence at scale on GPUs and TPUs.
• At the Staff level, set technical direction for model training and lead efforts across multiple teams.
• Work with Apple's researchers and ML engineers to train and ship models to Apple's quality and privacy standards.

What This Job Offers

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