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

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Not verified last checked Jun 13, 2026

Salary

Not verified not verified - source not recorded; timestamp not recorded

401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

Was this benefit information wrong? Tell us.

Schedule

Shift type

Not verified

Weekend work

Not verified

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

AIML - Staff ML Infrastructure Engineer, ML Platform & Technology - Pre-training Infrastructure San Francisco Bay Area, United States of America 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! 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

Read the full description at jobs.apple.com. FewerJobs shows a source-linked preview and links to the original posting.