ML Engineer, FM Training Integration - ML Platform Technologies

This role is for an ML Engineer within Apple's ML Compute team, focusing on training foundation models. The engineer will enhance the efficiency, scalability, and reliability of model training and inference in cloud environments. Responsibilities include integrating ML workloads with accelerator-based cloud infrastructure, performance tuning, debugging bottlenecks, benchmarking, and contributing to tools for improved observability and efficiency. The position requires a strong interest in scalable ML infrastructure and problem-solving.

Must Have

• Support integration of model training and inference workloads with accelerator-based cloud infrastructure.
• Assist with performance tuning of ML workloads to improve throughput, latency, and hardware utilization.
• Help identify and debug performance bottlenecks across data loading, model execution, and distributed training/inference.
• Collaborate with senior engineers to benchmark models and infrastructure configurations.
• Contribute to tooling, scripts, or pipelines that improve observability, reliability, and efficiency of ML workloads.
• Basic understanding of machine learning workflows (training, evaluation, inference).
• Familiarity with Python and at least one ML framework (e.g., PyTorch, TensorFlow, JAX).
• Basic knowledge of cloud computing concepts (e.g., VMs, containers, storage, networking).

Good to Have

• Exposure to GPU/TPU computing or accelerator-based workloads.
• Familiarity with distributed training or inference concepts (e.g., data parallelism, model parallelism).
• Experience with containerization or orchestration tools (e.g., Docker, Kubernetes).
• Basic understanding of profiling or benchmarking tools for ML workloads.
• Coursework or projects related to systems, cloud infrastructure, or performance engineering.

Perks & Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• A range of discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through discretionary employee stock programs
• Ability to purchase Apple stock at a discount through Employee Stock Purchase Plan
• Eligibility for discretionary bonuses or commission payments
• Eligibility for relocation

We are a group of engineers to support training foundation models at Apple! We build infrastructure to support training foundation models with general capabilities such as understanding and generation of text, images, speech, videos, and other modalities and apply these models to Apple products. We are looking for engineers who are passionate about building systems that push the frontier of deep learning in terms of scaling, efficiency, and flexibility and delight millions of users in Apple products.

We are looking for a ML Engineer to join our ML Compute team to help improve the efficiency, scalability, and reliability of model training and inference workloads in the cloud. In this role, you will work closely with senior ML engineers, infra engineers, and researchers to integrate ML workloads with cloud infrastructure, tune performance, and ensure effective utilization of the accelerators.

• Support the integration of model training and inference workloads with accelerator based cloud infrastructure.
• Assist with performance tuning of ML workloads to improve throughput, latency, and hardware utilization.
• Help identify and debug performance bottlenecks across data loading, model execution, and distributed training/inference.
• Collaborate with senior engineers to benchmark models and infrastructure configurations.
• Contribute to tooling, scripts, or pipelines that improve observability, reliability, and efficiency of ML workloads.

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• Basic understanding of machine learning workflows (training, evaluation, inference).
• Familiarity with Python and at least one ML framework (e.g., PyTorch, TensorFlow, JAX).
• Basic knowledge of cloud computing concepts (e.g., VMs, containers, storage, networking).
• Interest in performance optimization, systems efficiency, and scalable ML infrastructure.
• Strong problem-solving skills and willingness to learn complex systems.

• Exposure to GPU/TPU computing or accelerator-based workloads.
• Familiarity with distributed training or inference concepts (e.g., data parallelism, model parallelism).
• Experience with containerization or orchestration tools (e.g., Docker, Kubernetes).
• Basic understanding of profiling or benchmarking tools for ML workloads.
• Coursework or projects related to systems, cloud infrastructure, or performance engineering.

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 $147,400 and $220,900, 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.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.

Apple accepts applications to this posting on an ongoing basis.