ML Infrastructure Engineer - ML Compute Capacity

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 Qualifications7+ years of experience in relevant areasExperience with machine learning infrastructure on GPUs or TPUsProficiency in Python and/or Go for production backend and data engineering workExperience 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 systemsExcellent problem-framing and problem-solving skillsStrong CS fundamentalsBachelor’s degree or higher in Engineering, Mathematics, Economics, or a related quantitative fieldPreferred QualificationsExperience operating Kubernetes at production scale - including scheduling, resource management, and cluster debuggingExperience with modern web frameworks like ReactFamiliarity with accelerator utilization patterns across ML

training and inferenceStrong interest with capacity planning, cost attribution, or FinOps systemsPay & BenefitsAt 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 BenefitsNote: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.EEO StatementApple 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 applicantAccessibilityAt Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here - in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.Learn about accessibility in Apple’s workplaceLearn about reasonable accommodations for job applicantsApplication DeadlineApple accepts applications to this posting on an ongoing basis.

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.DescriptionAs 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.