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

Our team at the Huawei Computing Network Innovation Lab is looking for exceptional talent to join us and lead the development of next-generation data centers. We create cutting-edge technologies that synergize software and hardware in tandem to accelerate compute, storage, and networking at large scale. We aim to drive innovation and deliver software-defined infrastructure and algorithms for HPC, AI/ML, and Big Data applications.

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 backends and collective operations (AllReduce, AllGather, broadcast) for distributed training workloads

• Collaborate cross-functionally with hardware architects, compiler teams, and framework engineers to co-design hardware-software solutions

• Publish and present research findings at top international venues (NeurIPS, EuroSys, SC, MLSys, OSDI) and represent the team externally

• Mentor engineers and researchers, conduct performance and growth reviews, and shape team culture and technical direction

• Partner with top academic institutions and open-source communities to advance the state of the art in distributed AI systems

Why Join Us?

• Work on cutting-edge hardware (Huawei Ascend) at the intersection of systems software and AI research

• Publish and present at top international conferences with a team known for impactful research

• Lead a talented, international team in a fast-moving, high-impact environment

• Competitive compensation, research budget, and strong career growth into senior leadership roles

• 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 deliver results under research and engineering timelines

• Excellent communication skills in English — confident presenting to international audiences, writing technical reports, and driving cross-team alignment

• Strong collaborative mindset and experience working in globally distributed, multicultural teams

Ways to Stand Out From the Crowd

• M.Sc. or Ph.D. in a relevant field, with a strong publication record at systems or ML venues (EuroSys, OSDI, SC, NeurIPS, MLSys, ISCA)

• Hands-on experience with communication frameworks such as NCCL, MPI, HCCL, or UCX

• Experience with compiler and graph optimization for AI workloads (XLA, TVM, Triton, or custom operator fusion)

• Background in mixed-precision training, model parallelism (Tensor Parallelism, Pipeline Parallelism, Expert Parallelism), and large model co-design

• Experience profiling and debugging performance bottlenecks on heterogeneous clusters using tools like Chrome tracing, nsight, or custom profilers