Apple Cloud AI is at the forefront of ML and generative AI, where data and models come together to enable the next generation of intelligent experiences on Apple products and services. We build the scalable model training services and frameworks that accelerate the development of real-world AI applications, designed with both usability and efficiency in mind. Our platform covers the full model training lifecycle, from the first experiment to large-scale pre-training, fine-tuning, and reinforcement learning on thousands of GPUs and TPUs.
Our team focuses on applying the latest AI research to training, with user experience and accelerator efficiency in mind. You'll play a key role in shaping AI research at Apple and how its models are built, optimized, and scaled. We're looking for engineers and researchers who are passionate about research and building a high-performance model training stack that supports diverse real-world use cases. With the autonomy to experiment, the scale to make an impact, and the support to take ideas from prototype to production.
You'll work alongside a world-class team to build models and training services that make ML development faster, more reliable, and more efficient.
The Apple Cloud AI Platform team works with Apple's researchers and ML engineers to train and ship models at Apple's bar for quality and privacy. In this role, you will bring deep hands-on experience in large-scale model training to Apple's most important training efforts. You will develop training recipes for pre-training, fine-tuning, reinforcement learning, and model distillation, and apply techniques such as parameter-efficient fine-tuning, preference optimization, quantization-aware training.
You will diagnose loss spikes and numerical instability, and choose the precision, parallelism, and optimizer settings that let models converge reliably at scale on GPUs and TPUs. As a Staff ML/Research Engineer, you will set the technical direction for how Apple trains its models and lead efforts that span multiple teams.
Work with researchers and ML engineers to develop cutting-edge ML models, and turn their needs into training features and performance improvements that every team on the platform 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 Apple's data and governance stack so that every training run meets privacy and security 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.
Job description provided by Apple.
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