Senior ML Engineer, Apple Ray, Apple Data Platform

Apple Inc

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Cupertino, CA
Salary: $150,400–$277,600 / yr
Posted: 6 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $232k

This role $214k

$132k most similar roles pay here $324k

This role pays less than 65% of similar roles. Most pay $208,575–$254,750 — the shaded band above. At the midpoint, this role pays about $214k versus about $232k for comparable roles.

Based on 240 similar postings.

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About Apple Inc

Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software

Apple Inc currently has 3513 open roles on FindRole.

Listed pay typically runs $165,800–$277,600 across 2730 roles with salary data.

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At a glance

TL;DR · Senior ML Engineer, Apple Ray, Apple Data Platform

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Senior ML Engineer, Apple Ray, Apple Data Platform is a hybrid role within the Apple Ray team focused on building core components of a unified data and machine learning platform powered by open-source Ray. You will design and build distributed systems, system APIs, and orchestration layers to support large-scale model training, tuning, and inference across heterogeneous compute environments including GPU clusters. Day-to-day responsibilities include developing libraries to improve developer productivity, optimizing resource utilization, and designing fault-tolerant mechanisms for distributed workloads. The role requires proficiency in Python and a systems-level language like C++, Rust, Go, or Java, alongside experience with PyTorch, TensorFlow, and Kubernetes. You will solve complex problems regarding the performance and reliability of ML pipelines while contributing to architectural decisions and upstream enhancements for Ray and related tools.

Skills

Ray Python C++ Rust Go Java PyTorch TensorFlow Kubernetes Distributed Systems GPU DeepSpeed Horovod FSDP ZeRO Spark Flink

What you'll do

Build scalable distributed systems and platform components using Ray to power data and ML workflows.
Develop APIs, libraries, and services to improve the efficiency of large-scale training and inference pipelines.
Optimize performance and resource utilization for ML workloads across GPU and CPU clusters.
Design fault-tolerant orchestration mechanisms and autoscaling strategies for distributed ML jobs.
Diagnose complex issues across distributed systems and ML pipelines to ensure reliability.
Improve observability, monitoring, and debugging capabilities for ML-centric distributed workloads.
Contribute to architectural decisions and upstream enhancements for Ray and related tools.

What we're looking for

5+ years of experience 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 and strong experience in a systems-level language like 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.
B.S., M.S., or Ph.D. in Computer Science, Machine Learning, or a related technical field, or comparable experience.
Experience with distributed training frameworks, GPU optimization, model orchestration, open-source contributions, or large-scale data systems (preferred).