Large Machine Learning Model Optimization Engineer

SummaryPosted: Role Number:200571124Our team is an applied research and engineering team responsible for developing real-time on-device Language, Computer Vision, and Machine Perception technologies across Apple products. We focus on technology research and development to deliver Apple quality, state-of-the-art experiences. Our team prides itself on innovating through the full stack, and partnering with HW, SW and ML teams to influence the sensor and silicon roadmap that brings our vision to life. We are directly responsible for the on-device optimization and deployment of the Apple Intelligence LLM and diffusion models. As a Machine Learning Engineer, you will have the opportunity to be at the forefront of technological advancements and contribute to the successful shipping and delivery of Apple intelligence. You will be responsible for implementing and delivering various optimization techniques that improve the performance of large language and diffusion models on devices. Additionally, you will collaborate with a diverse range of organizations within Apple. Your innovations will significantly impact the entire ML model lifecycle of Apple intelligence.DescriptionWe’re looking for strong ML software engineers/leaders to drive the development of the on-device Apple Intelligence LLM and diffusion model developments. This includes defining and leading the execution of model compression, distillation, and integrating to the full Apple Intelligence user experiences. We expect you to have strong, efficient ML model development experiences and a passion for shipping machine learning models on device. We also encourage publishing novel research at top ML conferences.Minimum QualificationsSoftware engineering skills in PythonExperience in developing large computer vision and machine learning models, particularly on the hardware-aware model optimizationsBS and a minimum of 10 years relevant industry experiencePreferred QualificationsFamiliar with model compression algorithms including quantization, pruning, distillations, and experience on optimizing large diffusion models or language modelsMS or PhD degree in Computer Science, or equivalent industry research experienceExperience with hardware architecture, software & hardware co-designLeadership experience in driving large-scale projects in the industryStrong communication skills; phenomenal work ethic and collaborationPay & 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 $166,600 and $296,300, 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 take affirmative action to ensure 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.