ML Infrastructure Engineer - ML Compute Capacity

Role Number:
Team:
Machine Learning and AI

Location:

Santa Clara Posted:
Summary Scaling machine learning workloads across thousands of accelerators creates challenges that few engineers ever encounter. In Apple’s Machine Learning Platform Technologies organization, we build the infrastructure that powers large-scale ML training and inference workloads, bringing together expertise in distributed systems, machine learning infrastructure, and high-performance computing.

Description As an engineer on the ML Compute Capacity team, you will design, build, and operate the production systems that ensure compute resources are optimally distributed throughout the company. You'll work across the stack — from data pipelines and backend services to APIs and interactive frontends — developing telemetry systems, optimization algorithms, policies, and intuitive tools for managing demand and improving efficiency across Apple's largest accelerator fleet.

Our small, nimble team works in a high-autonomy, fast-paced environment, and we're passionate about digging into data patterns, laying out the performance characteristics of an entire distributed system, and knowledge sharing. If the opportunity to own and operate services that scale, stay highly available, and "just work" excites you, then please reach out to us!

Minimum Qualifications 7+ years of experience in relevant areas

Experience with machine learning infrastructure on GPUs or TPUsProficiency in Python and/or Go for production backend and data engineering work Experience building data pipelines and crafting robust queries over large-scale, multi-source data (e.g., Trino, PostgreSQL, Elasticsearch)

Experience with observability tools (e.g., Prometheus, Grafana) or equivalent monitoring systems

Excellent problem-framing and problem-solving skills

Strong CS fundamentals

Bachelor's degree or higher in Engineering, Mathematics, Economics, or a related quantitative field

Preferred Qualifications Experience operating Kubernetes at production scale — including scheduling, resource management, and cluster debugging

Experience with modern web frameworks like React Familiarity with accelerator utilization patterns across ML training and inference

Strong interest with capacity planning, cost attribution, or Fin Ops systems

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 $184,700 and $324,800, 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.

EEO Statement 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

Accessibility

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Application Deadline Apple accepts applications to this posting on an ongoing basis.