AIML - Staff Machine Learning Engineer - ML Efficiency, ML Platform & Technology

AIML - Staff Machine Learning Engineer - ML Efficiency, ML Platform & Technology Santa Clara, California, United States Machine Learning and AI Do you want to shape the platform that enables the next generation of intelligent experiences on Apple products & services? In Apple’s Machine Learning Platform Technology & Infra team we have built the platform that Apple uses for developing machine learning, artificial intelligence, and computer vision applications. As a team, we have a variety of technical backgrounds, from machine learning PhDs to builders of large-scale production systems. Specifically in this role you will be working on optimizing end-to-end system performance of distributed machine learning workloads. This is a highly collaborative role and you will be working with key partners across the company. Description We are seeking highly motivated and experienced engineers to join our team. The ideal candidate will have a deep understanding of machine learning systems and cloud computing infrastructure. Key responsibilities in this role are: Engaging with ML researchers to optimize end-to-end performance of large scale distributed ML workloads, Analyzing workload metrics to identify sources of inefficiencies and working with users to understand and optimize ML workloads, Conducting workload analysis based on benchmarking key workloads on deployed systems, Improving large scale training resiliency by optimizing applications and frameworks for improved recovery from failures and preemptions, Influencing architecture, design, development, and operations of next generation ML accelerator systems based on workload insights. Minimum Qualifications Experience working with large scale parallel and distributed accelerator-based systems Experience optimizing performance and AI workloads at scale Experience developing code in one or more of training frameworks (such as PyTorch, TensorFlow or JAX) Strong communicator with ability to analyze complex and ambiguous problems Programming and software design skills (proficiency in C/C++ and/or Python) Experience working in a high-level collaborative environment and promoting a teamwork mentality Bachelor’s degree in Computer Science and 7+ years of work experience Preferred Qualifications Deep understanding of computer systems and the interactions between HW and SW Experience in performance analysis and optimization experience in Cloud accelerators Advanced degree in CS 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. 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. #J-18808-Ljbffr