AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - ML Compute

Role Number : 200623794-3401

Summary

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something!

Description

As a staff engineer on ML Compute team, your work will include :

Lead the development of the infrastructure to run large-scale workloads on the Cloud, such as Apache Spark, Ray, and distributed training.

Optimize platform efficiency and throughput by improving resource management capabilities with schedulers like Apache YuniKorn and Kueue.

Integrate new features from core distributed computing and ML frameworks into the platform, offering them to production users and providing support.

Enhance the platform's scalability, performance, and observability through improved monitoring and logging.

Drive the architectural evolution of the platform by adopting modern, cloud-native technologies to improve system performance, efficiency, and scalability.

Reduce dev-ops efforts by automating and streamlining operational processes.

Mentor engineers in areas of your expertise, fostering skill growth and knowledge sharing.

Minimum Qualifications

Bachelors in Computer Science, engineering, or a related field

6+ years of hands-on experience in building scalable backend systems for training and evaluation of machine learning models

Proficient in relevant programming languages, like Python or Go

Strong expertise in distributed systems, reliability and scalability, containerization, and cloud platforms

Proficient in cloud computing infrastructure and tools : Kubernetes, Ray, PySpark

Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find solutions

Preferred Qualifications

Advance degrees in Computer Science, engineering, or a related field.

Hands-on experience with cloud-native resource management and scheduling tools like Apache YuniKorn.

Experience with advanced architecture for distributed data processing and ML workloads.

Proficient in working with and debugging accelerators, like : GPU, TPU, AWS Trainium.

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