Senior ML Engineer, Apple Ray, Apple Data Platform

Apple Inc.; Cupertino, Northern (CA, KY); Hybrid; USD 150,000 - 278,000; Full time

Benefits offered by this job

Stock options

Discretionary bonuses

Relocation available

Educational reimbursements

Healthcare benefits

Job summary

Apple Inc. is seeking a Senior/Staff Software Engineer to design and build core components of Apple’s unified data+ML platform powered by Ray.

This hybrid role requires strong distributed systems expertise and ML fluency, supporting large-scale training and inference workloads across diverse compute environments. You will contribute to the runtime, orchestration layer, and APIs that power Apple’s intelligent features, collaborating with ML teams to translate model needs into robust platform

Qualifications

• 5+ years building distributed systems or high-scale backend services.
• Strong ML workflows, model training, serving, or data pipeline work.
• Fluency in Python plus a systems language (C++, Rust, Go, or Java).
• Experience with ML frameworks such as PyTorch or TensorFlow and GPU training.
• Understanding of parallelism, scaling, or distributed training concepts.
• Experience with cluster orchestration (Kubernetes, EKS, GKE) or large compute systems.
• BS/MS/PhD in CS/ML or related fields — or equivalent software experience.

Responsibilities

• Build scalable distributed systems and platform components using Ray for data+ML workflows.
• Develop APIs, libraries, and services for large-scale ML training and inference pipelines.
• Optimize performance and resource use across GPU/CPU clusters for ML workloads at scale.
• Collaborate with ML teams to translate model/pipeline needs into platform features.
• Design fault-tolerant orchestration, autoscaling, and runtime improvements for distributed ML jobs.
• Diagnose complex issues across distributed systems and ML pipelines for reliability.
• Improve observability, monitoring, and debugging for ML-centric workloads.
• Contribute to architectural decisions and upstream Ray enhancements.

Skills

Distributed systems

ML workflows

Python

C++

Rust

Go

Java

PyTorch

TensorFlow

GPU training

Parallelism

Education

BS/MS/PhD in CS, ML or related fields

Tools

Ray

Kubernetes

GKE

EKS

Job description

Cupertino, California, United States Software and Services

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.

Responsibilities

• Build scalable distributed systems and platform components using Ray that power Apple’s data+ML workflows.
• Develop APIs, libraries, and services that improve the efficiency and usability of large-scale ML training and inference pipelines.
• Optimize performance and resource utilization across GPU/CPU clusters for ML workloads running at Apple scale.
• Collaborate with ML teams to understand model and pipeline needs and translate them into robust platform features.
• Design fault-tolerant orchestration mechanisms, autoscaling strategies, and runtime improvements for distributed ML jobs.
• Diagnose complex issues across distributed systems and ML pipelines to ensure reliability and availability.
• Improve observability, monitoring, and debugging capabilities targeted at ML-centric distributed workloads.
• Contribute to architectural decisions and, where appropriate, upstream enhancements to Ray and related tools.

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.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here — in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

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Apple accepts applications to this posting on an ongoing basis.