Collaborate with domain experts to efficiently map large ML workloads to distributed systems. Understand the performance sensitivity of these workloads to networking parameters like bandwidth, latency, topology, routing, and error rate.
Develop architecture specifications for networking IP.
Collaborate with IP architecture, logic design, verification, firmware, software, and system teams to ensure end-to-end successful execution.
BS degree
Understanding of computer architecture and HW/SW partitioning
Experience in architecting, designing and verifying high performance ASICs
Understanding of distributed AI/ML workloads and data flows
Understanding of HW and SW aspects of data center networking technologies like Ethernet, TCP/IP, RDMA/RoCE, NVLink, or similar
20+ years of relevant experience
Understanding of state-of-the-art LLMs and how they use the compute, memory, and networking resources of modern machines
Understanding of emerging networking standards
Understanding of large-scale network behavior and its effect on high-performance distributed applications
Understanding of network topologies, traffic management techniques, QoS, and error recovery
Experience with networking hardware design
Familiarity with relevant SW technologies like NCCL, UCX/UCC, PyTorch/JAX, MPI, SHMEM, and libfabric
Ability to study a problem in depth, design experiments, analyze data, present results, and collaborate effectively across disciplines and geographies
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.