Senior Researcher - Edge AI Optimization/Hardware-Aware ML

Huawei Canada has an immediate permanent opening for a Researcher.

About the team

The Software-Hardware System Optimization Lab focuses on research and innovation in power efficiency and performance optimization for consumer devices. By leveraging the talents and capabilities of local academia and our team, we aim to build system-optimization capabilities for software and hardware across edge AI, multimedia, graphics, mobile gaming, and system software domains, thereby enhancing the user experience and performance competitiveness of Huawei's consumer device products.

About the job

• Conduct research in hardware-aware neural network optimization (e.g., quantization-aware training, mixed precision, pruning, distillation, neural architecture search).
• Develop novel approaches for latency/energy-aware training objectives and Pareto optimization (accuracy vs. compute vs. memory).
• Prototype and evaluate techniques for efficient inference under device constraints (thermal limits, memory bandwidth, intermittent connectivity).
• Publish and present findings internally and externally (papers, workshops, patents, technical blogs).
• Optimize inference pipelines across pre/post-processing, scheduling, operator fusion, memory planning, and runtime execution.
• Collaborate on or contribute to compilers / runtimes (e.g., TVM, MLIR, XLA, TensorRT, ONNX Runtime, TFLite, ExecuTorch) to improve operator coverage and performance.
• Profile and optimize models with real device traces, addressing bottlenecks such as cache misses, memory bandwidth, kernel launch overhead, and CPU–NPU handoff.
• Build and maintain hardware-aware benchmarking methodology and regression suites for edge targets (ARM CPU, mobile GPU, DSP, NPU).
• Create deployment recipes for heterogeneous compute (CPU+GPU+NPU) including partitioning strategies and fallback paths.
• Drive optimization for on-device personalization and incremental updates when needed (e.g., small adapters, efficient fine-tuning).
• Partner with product engineering, platform teams, and hardware teams to translate device constraints into research targets and to transition research prototypes into production.
• Mentor junior researchers/engineers, review experimental designs, and raise the quality bar for measurement rigor and reproducibility.
• Define technical roadmap areas (e.g., next-gen quantization, kernel optimization, model families for edge, compiler improvements).