Senior AI System Engineer - LLM Training System
Huawei Canada has an immediate permanent opening for a Senior Engineer.
About the team
The Computing Data Application Acceleration Lab aims to create a leading global data analytics platform organized into three specialized teams using innovative programming technologies. This team focuses on full-stack innovations, including software-hardware co-design and optimizing data efficiency at both the storage and runtime layers. This team also develops next-generation GPU architecture for gaming, cloud rendering, VR/AR, and Metaverse applications. One of the goals of this lab are to enhance algorithm performance and training efficiency across industries, fostering long-term competitiveness.
About the job
As a Senior System Engineer, you will drive core compute operator development and distributed scaling performance across foundation model pre-training and post-training workloads on Ascend NPUs. You will identify computational and communication bottlenecks, implement high-performance kernels using AscendC, Triton, and TileLang, and optimize distributed acceleration suites (MindSpeed / open-source training frameworks).
Ascend High-Performance Kernel Development
Design, implement, and tune core operators (FlashAttention variants, FP8/MXFP8/W4A8 GEMM, Fused RMSNorm, dynamic MoE routing/dispatch) using AscendC, with explorations in Triton (Triton-Ascend backend) and TileLang.
Pre-training & Post-training System Optimization
Architect, scale, and optimize distributed pre-training and RL post-training pipelines using MindSpeed (MindSpeed-LLM / MindSpeed-MM) alongside upstream frameworks (Megatron-LM / Megatron-Core, PyTorch FSDP, veRL, vLLM, Ray).
Distributed Parallelism & Training Efficiency
Tune multi-dimensional hybrid parallelism (TP, PP, CP, EP, ZeRO/FSDP) and RL rollout/actor pipelining on Ascend NPU clusters, achieving optimal MFU and compute-communication overlap.
Low-Precision & Numerical Parity
Implement low-precision training/inference schemes (FP8, MXFP8, mixed-precision), guaranteeing numerical stability, batch-invariance, and strict precision parity between training and serving runtimes.
Profiling & Hardware-Aware Tuning
Conduct deep performance profiling on Ascend hardware using Ascend Profiler (msprof), msprobe, and PyTorch Profiler to eliminate pipeline bubbles, AICore/AIVector stalls, and HCCL communication overhead.
Open Source & Ecosystem Enablement
Contribute high-performance operators, bug fixes, and acceleration features to open-source Ascend communities and upstream AI acceleration projects. The total target annual compensation ranges from $127,000 to $225,000 depending on education, experience, and demonstrated expertise.
About the ideal candidate
Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related technical disciplines, 3+ years of experience in high-performance computing (HPC), AI accelerator operator development, or distributed large model training systems.
Hands-on Operator Programming
Solid development experience in AscendC / CANN, or extensive experience in CUDA C/C++ with a proven ability to transition to AscendC. Experience or strong interest in Triton or TileLang for accelerator backends.
Distributed Frameworks
Deep practical experience with distributed pre-training and/or post-training frameworks, such as MindSpeed, Megatron-LM / Megatron-Core, PyTorch FSDP, veRL, or vLLM / DeepSpeed.
Systems Programming
Strong programming proficiency in C/C++ and modern Python, with hands-on experience building PyTorch custom C++/CANN extensions.
Performance Analysis
Proficiency in performance profiling and debugging tools on AI hardware (e.g., Ascend Profiler/msprof, PyTorch Profiler, or Nsight toolchains).
Preferred / Standout Qualifications
Direct experience porting or optimizing Triton or TileLang frontends/backends for non-CUDA accelerators (especially Ascend NPU). Deep understanding of Ascend NPU microarchitecture (Cube & Vector compute units, local memory hierarchy/UB, memory movement engines, and HCCL collective communication). Hands-on experience optimizing end-to-end MFU for massive MoE or dense foundation models on multi-node NPU/GPU clusters. Experience in low-precision numerical alignment (FP8, MXFP8, W4A8/W8A8) and eliminating training-inference discrepancy.
Active open-source contributions to Ascend repos (MindSpeed, CANN ops, Ascend-vLLM) or upstream projects (Megatron-LM, Triton, TileLang, veRL). Additional Information： Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know and we will work with you to meet your needs. All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates.