About this role
Apple’s Server ML Frameworks team in Intelligent System Experience works on enabling Apple Intelligence through high-performance, distributed inference of GenAI applications (such as LLMs) on Private Cloud Compute. You will get to work on custom-built server hardware that brings the power and security of Apple silicon to the data center. We are looking for a senior technical leader with strong technical background who is deeply passionate about building scalable, efficient, and production-grade solutions tailored for high-throughput GPU execution and to partner with software and hardware leaders to define Server Apple Intelligence roadmap.
As a senior technical leader on Apple's Server ML Frameworks team, you will set the technical direction and software architecture for the frameworks that power Apple Intelligence at scale. You will partner with teams across Apple to propose, develop, and evaluate features in new Apple Intelligence models; collaborate closely with our hardware organizations to influence the design of next-generation silicon; and help define the compute and programming models for future server architectures. You will also grow and mentor a team of engineers, establish engineering best practices, and build the cross-functional relationships needed to deliver complex, multi-team initiatives.
Minimum Qualifications
BS/MS/PhD in Computer Science or a related field, or equivalent practical experience
3–5 years of experience as a manager or technical leader
Experience with GPU kernel development and optimization using compute programming models such as Metal, CUDA, or similar
Experience with distributed training or inference techniques
Strong system-level programming skills in C/C++, with a solid understanding of operating systems and computer architecture
Preferred Qualifications
Track record in strategic leadership and decision-making
Familiarity with Apple framework and API design patterns
Experience with graph compilers such as CuTE, CuTile, Triton, OpenXLA or LLVM is a plus
Good understanding of LLM and Diffusion based model architectures