GPU ML Engineer

Apple·Cupertino, California, United States·posted 24d ago · last seen 42m ago

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One of tech's least standardized loops: you interview for a specific team, and every stage belongs to it. Recruiter/hiring-manager screen, one to three 45–60 minute Coderpad coding screens, a system design round shaped by Apple's reliability and privacy constraints, and a behavioral round — then a panel debrief. There is no central question bank; questions map to the team's real stack.

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Description

Summary

Apple’s Compute Frameworks team in GPU, Graphics and Displays org provides a suite of high-performance data parallel algorithms for developers inside and outside of Apple for iOS, macOS and Apple TV. Our efforts are currently focused in the key areas of linear algebra, image processing, machine learning, along with other projects of key interest to Apple. We are always looking for exceptionally dedicated individuals to grow our outstanding team.

Description

Our team is seeking extraordinary machine learning and GPU programming engineers who are passionate about providing robust compute solutions for accelerating machine learning networks on Apple Silicon. Role has the opportunity to influence the design of compute and programming models in next generation GPU architectures.

Key Responsibilities

Adding optimized GPU compute kernels across Machine Learning, Image Processing, Linear Algebra and Computer Vision. Defining and implementing APIs in Metal Performance Shaders. Performing in-depth analysis, compiler and kernel level optimizations to ensure the best possible performance across hardware families. Partnering with Platform Architecture teams to define Apple GPU's compute hardware roadmap. Working with hardware team to analyze performance on future silicon Tune GPU-accelerated compute across products.

Minimum Qualifications

Technical BS/MS degree and Equivalent experience. 2 plus years of experience GPU compute kernel framework development, maintenance, and optimization. Experience with system level programming and computer architecture. Experience with high performance parallel programming, GPU programming experience

Preferred Qualifications

Excellent programming and problem-solving skills. Strong communication skills. Strong collaboration skills. Good understanding of machine learning fundamentals. Background in mathematics, including linear algebra and numerical methods is a plus.

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