About this role
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.