About the role

What will you do at Apple?

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

5+ 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.

Which skills does this role require?

PythonC++GPU trainingData pipelinesSystem APIsCloud-native infrastructureApple RayData platformRayModel trainingModel inferenceCompute runtimesBackend services

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