Senior / Staff ML Engineer, Apple Ray, Apple Data Platform

Summary

The Apple Ray team is seeking a Senior / Staff Software Engineer with strong distributed systems expertise and a solid background in machine learning. In this hybrid role, you will design and build core components of Apple’s unified data+ML platform powered by open-source Ray, while also partnering with ML teams to ensure the platform meets the needs of large-scale training and inference workloads. You will contribute to the distributed runtime, orchestration layer, and system APIs that power Apple’s intelligent features across products and services. This role is ideal for a software engineer who enjoys low-level systems work but is also fluent in ML workflows and models at scale.

Description

Apple Ray integrates deeply with Apple’s data and ML ecosystem to provide a unified platform for building, orchestrating, and scaling complex ML and data pipelines. As a Software Engineer with ML background, you will design distributed systems that support large-scale model training, tuning, and inference across heterogeneous compute environments—from bare-metal GPU clusters to cloud-native infrastructure. You will build features that enhance developer productivity for ML engineers, improve resource efficiency, and advance the performance and reliability of Apple’s ML workloads. You’ll collaborate closely with ML practitioners to translate model and pipeline needs into robust platform capabilities, while also improving the underlying distributed runtime and control plane. This role requires strong engineering fundamentals, hands-on experience with ML systems, and a passion for building scalable infrastructure.

Minimum Qualifications

6+ years building distributed systems, high-scale backend services, or compute runtimes. Solid background in ML workflows, model training, model serving, or data pipeline development. Proficiency in Python, plus strong experience in a systems-level language (C++, Rust, Go, or Java). Experience with ML frameworks such as PyTorch or TensorFlow and familiarity with GPU-based training. Understanding of parallelism strategies, model scaling, or distributed training concepts. Experience with cluster orchestration (Kubernetes, EKS, GKE) or large-scale compute systems. Strong debugging skills across distributed and ML-centric runtime environments. Ability to work cross-functionally with ML engineers, data engineers, and infrastructure teams. B.S., M.S., or Ph.D. in Computer Science, Machine Learning, or related technical fields — or comparable software engineering experience.

Preferred Qualifications

Experience with distributed training frameworks (DeepSpeed, Horovod, FSDP, ZeRO). Background in optimizing GPU workloads or performance benchmarking. Experience with model orchestration systems or ML platforms. Contributions to open-source ML or distributed systems projects. Familiarity with large-scale data systems such as Spark, Flink, or similar.