Senior AI Infra Performance Engineer
Scaling machine learning workloads across thousands of GPUs and TPUs 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 a performance engineer in the ML Compute Efficiency team, you'll tackle ambiguous systems challenges, identify inefficiencies and build solutions that maximize accelerator utilization, reduce idle and fragmented capacity, and minimize recovery periods. This includes analyzing accelerator performance, digging into various parallelism techniques, and refining workload scheduling and orchestration across the compute fleet.","responsibilities":"Characterize ML workload behavior through profiling, benchmarks and metrics.
Dive into unfamiliar codebases to prototype changes, evaluate tradeoffs, and build production-ready solutions.
Design systems for efficient recovery from failures and preemptions.
Create tools to identify and alert bottlenecks across applications and frameworks.
Use workload-driven insights to influence next-generation hardware selection and procurement decisions.
Collaborate closely with ML researchers and infrastructure engineers to address inefficiencies.
Drive impact through hands-on contribution and mentorship.
Preferred Qualifications
Have a track record of delivering transformative performance improvements on large scale infrastructure.
Ability to analyze ambiguous, distributed systems problems and articulate both high-level strategic metrics and underlying technical complexity.
Minimum Qualifications
Experience with large-scale distributed systems for AI/ML workloads running on GPUs or TPUs.
Strong software engineering skills with experience developing and optimizing training frameworks (e.g. PyTorch, JAX) using C/C++ or Python.
Experience working on cross-functional projects with ML research and infrastructure teams.
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Familiarity with model architectures and various training techniques.
Bachelor's degree in Computer Science or equivalent experience, with 7+ years of industry experience.
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 .
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.
Perks and Benefits
Health and Wellness
Parental Benefits
Work Flexibility
Office Life and Perks
Vacation and Time Off
Financial and Retirement
Professional Development
Diversity and Inclusion
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