AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - Pre-training Infrastructure

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 an engineer on ML Compute team, your work will include:

- Drive large-scale pre-training initiatives to support cutting-edge foundation models, focusing on resiliency, efficiency, scalability, and resource optimization.

- Enhance distributed training techniques for foundation models.

- Research and implement new patterns and technologies to improve system performance, maintainability, and design.

- Optimize execution and performance of workloads built with JAX, PyTorch, XLA and CUDA on large distributed systems.

- Leverage high-performance networking technologies such as NCCL for GPU collectives and TPU interconnect (ICI/Fabric) for large-scale distributed training.

- Architect a robust MLOps platform to streamline and automate pretraining operations.

- Operationalize large-scale ML workloads on Kubernetes, ensuring distributed trainings are robust, efficient, and fault-tolerant.

- Lead complex technical projects, defining requirements and tracking progress with team members.

- Collaborate with cross-functional engineers to solve large-scale ML training challenges.

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

- Cultivate a team centered on collaboration, technical excellence, and innovation.

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

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

Pay & Benefits

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 $181,100 and $318,400, 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.