Senior / Staff Machine Learning Engineer, Apple Cloud AI Platform

Apple • Seattle, WA

2d

About The Position

Apple Cloud AI builds scalable model training services and frameworks for developing AI applications across Apple products and services. The platform supports the full model training lifecycle, from initial experiments to large-scale pre-training, fine-tuning, and reinforcement learning across thousands of GPUs and TPUs, with a focus on usability, efficiency, and privacy.

The Senior / Staff Machine Learning Engineer will bring hands-on experience in large-scale model training to important Apple training efforts. The role develops training recipes for pre-training, fine-tuning, reinforcement learning, and model distillation, and applies methods such as parameter-efficient fine-tuning, preference optimization, and quantization-aware training. It also diagnoses loss spikes and numerical instability and selects precision, parallelism, and optimizer settings that enable reliable model convergence at scale.

The engineer will collaborate with Apple researchers and ML engineers to turn their needs into reusable training features and performance improvements. At the Staff level, the role sets technical direction for model training and leads work spanning multiple teams, while mentoring engineers and raising the engineering bar.

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 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 in Computer Science, Computer Engineering, or equivalent practical experience.

Nice To Haves

• Experience with low-precision training and its numerical challenges.
• 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 these affect 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

• Work with researchers and ML engineers to develop machine learning models and turn their needs into training features and platform performance improvements.
• Develop model distillation and compression methods, including teacher-student distillation and quantization-aware training.
• Train models using long-context extension, mixture-of-experts architectures, and multimodal inputs, and address their stability and efficiency challenges.
• Investigate training behavior at scale, including loss spikes, divergence, and throughput drops, and identify root causes in data, numerical behavior, code, or hardware.
• Build and own scalable, efficient training frameworks and services for GPUs and TPUs, using PyTorch or JAX and higher-level frameworks.
• Build reinforcement learning infrastructure for post-training, including rollout and inference services, reward pipelines, and secure sandboxes for models to run code and use tools.
• Integrate the training platform with Apple's data and governance stack to ensure training runs meet privacy and security standards.
• Set technical direction across teams, mentor engineers, and raise the engineering bar.

What This Job Offers

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