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# Senior / Staff Machine Learning Engineer, Apple Cloud AI Platform

- **Company:** Apple Inc.
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Computing, Profiling, Computer Programming, Computer Engineering, Distributed Computing Environment, InfiniBand, Python (Programming Language), Machine Learning, Open Source Technology, Software Engineering, Reinforcement Learning, Graphics Processing Unit (GPU), Pytorch, DeepSpeed, Parallel Computation, AI Platforms, Information Technology, Megatron
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-staff-machine-learning-engineer-apple-cloud-ai-platform-seattle-wa--3e6f5cc6-c317-42cf-8b69-a41a015ff127

## About the Role

Software Engineering, Machine Learning, Artificial Intelligence (AI), Cloud Computing, Apple, Reinforcement Learning, Prototyping, Usability Engineering, Use Cases, Artificial Intelligence (AI), standards. Set technical direction across teams, mentor engineers, and raise the engineering bar.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

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 with 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.

## Description

can use. Develop model distillation and compression methods, such as teacher-student distillation and quantization-aware training, that bring the quality of large models to smaller models. Train models with long-context extension, mixture-of-experts architectures, and multimodal inputs, and solve the stability and efficiency problems each one brings. Dig into training behavior at scale, such as loss spikes, divergence, and drops in throughput, and track each problem down to its root cause in the data, the numerics, the code, or the hardware. Build and own scalable, efficient training frameworks and services on GPUs and TPUs, built on PyTorch or JAX and on higher-level frameworks. Build reinforcement learning infrastructure for post-training, including rollout and inference services, reward pipelines, and secure sandboxes where models can run code and use tools. Integrate the training platform with Apples data and governance stack so that every training run meets privacy and security