AIML - ML Infrastructure Engineer, ML Platform & Technology - ML Compute at Apple

Seattle, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

13 Jan, 26

Salary

0.0

Posted On

15 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Backend Systems, Distributed Systems, Reliability, Scalability, Containerization, Cloud Platforms, Kubernetes, Ray, PySpark, Python, Go, GPU, TPU, AWS Trainium, JAX, Tensorflow, PyTorch, TensorRT, vLLM

Industry

Computers and Electronics Manufacturing

Description

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 Senior/Staff Engineer on the Foundation Model Compute Infra team, you will design and scale the scheduling and orchestration systems that power Apple’s large-scale foundation model training and inference workloads across TPU clusters. You will drive innovations in resource management, cluster efficiency, and platform reliability, enabling Apple’s next-generation AI models to train and serve at scale. MINIMUM QUALIFICATIONS Bachelors in Computer Science, engineering, or a related field 5+ 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 PREFERRED QUALIFICATIONS Advance degrees in Computer Science, engineering, or a related field Proficient in working with and debugging accelerators, like: GPU, TPU, AWS Trainium Proficient in ML training and deployment frameworks, like: JAX, Tensorflow, PyTorch, TensorRT, vLLM

Responsibilities

Design and scale the scheduling and orchestration systems for large-scale foundation model training and inference workloads. Drive innovations in resource management, cluster efficiency, and platform reliability.