Do you feel you think differently, you are eager to break status quo, are bold and ambitious, aren't afraid to take risks and are passionate to build the best of class technology. If yes, what better place to be at and do this than Apple? At Apple, "we think different, we push the boundaries of computing and intelligence. We build products that bring smile to people's face". Foundation Model Infrastructure team, within Machine Learning Platform Technologies organization is the back-bone of Apple Intelligence. It builds frameworks, services and tools that power the largest Apple foundation models on servers. Our Infrastructure powers a wide gamut of services at Apple including Apple Search, Apple Music, AppleTV, AppStore, iMessages, Photos & Camera, Spotlight, Safari, Siri and upcoming ever 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 life of people. You will have a chance to work on optimizing billions of parameter languge and vision and speech models using state of the art technologies and make it run at scale of Apple.
Description Work along side Foundation Model Research team to optimize inference for cutting edge model architectures. Work closely with product teams to build Production grade solutions to launch models serving millions of customers in real time. Build tools to understand bottlenecks in Inference for different hardwares and use cases. Mentor and guide engineers in the organization.
Minimum Qualifications - 5+ years of experience leading and driving complex, ambiguous projects.
- Have experience with high throughput services particularly at supercomputing scale.
- Proficient with running applications on Cloud (AWS / Azure or equivalent) using Kubernetes, Docker etc.
- Familiar with GPU programming concepts using CUDA.
- Familiar with one of the popular ML Frameworks like Pytorch, Tensorflow.
Preferred Qualifications - Proficient in building and maintaining systems written in modern languages (eg: Golang, python)
- Familiar with fundamental Deep Learning architectures such as Transformers, Encoder/Decoder models.
- Familiarity with Nvidia TensorRT-LLM, vLLLM, DeepSpeed, Nvidia Triton Server etc.
- Experience writing custom CUDA kernels using CUDA or OpenAI Triton.
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.
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 .
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