Research Team Lead - Distributed AI Systems & Large-Scale Infrastructure

We are looking for an outstanding Research Team Lead with deep hands-on expertise in large-scale distributed systems, AI framework infrastructure, and performance optimization on custom accelerators. If you are a visionary technical leader, a skilled communicator, and a team builder who thrives at the frontier of systems and AI research - you're welcome on board.
What Will You Be Doing?
Lead and grow a world-class team of researchers and engineers working on distributed AI infrastructure and systems software
Architect and own the software infrastructure enabling distributed training and inference on Huawei's custom accelerator hardware (e.g., Ascend NPU)
Drive research and development on communication libraries, runtime systems, memory management, and graph execution & synchronization at scale
Optimize end-to-end performance across large-scale clusters, covering both scale-up (multi-device) and scale-out (multi-node) configurations
Design and implement high-performance communication b

דרישות:

B.Sc. or higher in Computer Science, Computer Engineering, Electrical Engineering, or a closely related field
8+ years of experience in systems software, distributed computing, or AI infrastructure, with 3+ years in a leadership or team lead role
Deep expertise in large-scale communication systems: collective communication, RDMA, network topology-aware routing, and bandwidth optimization
Hands-on experience building software infrastructure for distributed training on custom accelerators or heterogeneous hardware (GPU, NPU, TPU)
Strong knowledge of runtime systems: scheduling, execution graphs, kernel dispatch, synchronization primitives, and pipeline management
Experience with memory management at scale: activation checkpointing, tensor offloading, rematerialization, KV cache management
Proficiency in C/C++ and Python, with a focus on high-performance, production-quality code in Linux environments
Proven ability to define technical vision, lead multi-person projects end-to-end, and de