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GPU Machine Learning Engineer, Platform Architecture at Apple
Cupertino, California, United States -
Full Time

Start Date

Immediate

Expiry Date

29 May, 26

Salary

0.0

Posted On

28 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

GPU Programming, Machine Learning, Performance Analysis, Kernel Implementations, Metal, CUDA, MLX, Pytorch, Software Optimization, Hardware Optimization, LLM Inference, CUDA C++, Linear Algebra

Industry

Computers and Electronics Manufacturing

Description
Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish! Dynamic, inquisitive people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same passion for innovation that goes into our products also applies to our practices strengthening our commitment to leave the world better than we found it. DESCRIPTION The Platform Architecture GPU group is looking for a talented GPU Machine Learning Engineer to join the Neural Accelerator effort with strong skills in performance analysis and development at the level of ML frameworks and lower-level kernel implementations. MINIMUM QUALIFICATIONS BS degree Experience with software and hardware performance analysis and optimization Experience in GPU programming models such as Metal, CUDA, or similar Experience with ML frameworks, for example MLX, Pytorch, or similar PREFERRED QUALIFICATIONS MS or PhD in Computer Science, Electrical Engineering, or equivalent 3+ years of relevant industry experience Experience working specifically in CUDA C++ on ML and/or linear algebra algorithms Experience optimizing LLM inference for low latency at the implementation level Experience optimizing LLM inference at scale in the cloud or datacenter Ability to communicate across both hardware and software organizations
Responsibilities
The role involves joining the Neural Accelerator effort within the Platform Architecture GPU group, focusing on machine learning engineering tasks. This includes strong skills in performance analysis and development spanning ML frameworks down to lower-level kernel implementations.