Do you feel you think differently, are eager to break the status quo, are bold and ambitious, aren't afraid to take risks, and are passionate about building best-in-class technology? If yes, what better place to do this than Apple? At Apple, "we think different, we push the boundaries of computing and intelligence. We build products that bring smiles to people's faces." The Foundation Model Services team, within the Machine Learning Platform Technologies organization, is the backbone of Apple Intelligence. It builds frameworks, services, and tools that power the largest Apple foundation models on servers. Our infrastructure powers a wide range of services at Apple including Apple Search, Apple Music, Apple TV, App Store, iMessages, Photos & Camera, Spotlight, Safari, Siri, and upcoming exciting Apple products serving millions of queries every day with incredible low latencies, drawing every ounce of compute from our hardware. As part of this group, you will get a chance to bring intelligence to billions of users across the world. You will have an opportunity to make a difference in the lives of people. You will have a chance to work on optimizing billions of parameter language, vision, and speech models using state-of-the-art technologies and make them run at the scale of Apple.
Description * Work closely with product teams to build production-grade solutions to launch models serving millions of customers in real-time.
* Work alongside the Foundation Model Research team to prototype and develop inference for cutting-edge model architectures.
* Build tools to understand bottlenecks in inference for different hardware and use cases.
* Mentor and guide engineers in the organization.
Minimum Qualifications 8+ years of experience leading and driving complex, ambiguous projects.Strong industry background and experience in ML technologies (LLMs, Machine Learning, NLP, Information Retrieval, Statistics).Rich experience with high throughput services, particularly at supercomputing scale.Proficient with running applications on Cloud (AWS/Azure or equivalent) using Kubernetes, Docker, etc.Proficient in building and maintaining systems written in modern languages (e.g., Golang, Python).Preferred Qualifications Familiar with one of the popular ML Frameworks like PyTorch, TensorFlow.Familiar with fundamental Deep Learning architectures such as Transformers, Encoder/Decoder models.Familiarity with Nvidia TensorRT-LLM, vLLLM, DeepSpeed, Nvidia Triton Server, etc.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 $175,800 and $312,200, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become Apple shareholders 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 reimbursement for certain educational expenses related to advancing your career at Apple, 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 benefits, 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.
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