Researcher - AI

Huawei Canada has an immediate 12-month contract opening for a Researcher.

About the team:

The Software-Hardware System Optimization Lab continuously improves the power efficiency and performance of smartphone products through software-hardware systems optimization and architecture innovation. We keep tracking the trends of cutting-edge technologies, building the competitive strength of mobile AI, graphics, multimedia, and software architecture for mobile phone products.

About the job:

• Conduct state-of-the-art literature surveys, formulate novel research hypotheses, and prototype RL-driven optimization algorithms tailored for large-scale generative models (e.g., Large Language Models, Vision-Language Models, and Diffusion-based Image/Video Generation Models) deployed on resource-constrained edge devices.
• Explore next-generation application paradigms where RL can unlock unprecedented on-device intelligence and performance.
• Collaborate closely with researchers to prototype, implement, and integrate research ideas into production-grade code.
• Design and run rigorous experiments on internal and public benchmarks; build reproducible pipelines, ablations, and statistical analyses.
• Stay current with literature; distill insights; propose and drive new directions.

About the ideal candidate:

• MS/PhD in Computer Science, Computer Engineering, Artificial Intelligence, Applied Mathematics, or a related quantitative field.
• Solid theoretical foundation in Reinforcement Learning (MDPs, deep RL algorithms like PPO, SAC, DQN, policy gradients, or model-based RL).
• Demonstrated research experience or publication record in top AI/ML/System conferences (NeurIPS, ICML, ICLR, MLSys, CVPR, AAAI, etc.).
• Strong hands-on experience with Deep Learning frameworks (PyTorch, JAX, or TensorFlow) and proficiency in Python and Linux/Unix development environments (e.g., Ubuntu), and C++ or CUDA programming for low-level system profiling and operator optimization.
• Familiarity with modern generative model architectures (Transformers, Diffusion models, Vision Transformers).
• Understanding of hardware constraints in edge environments (e.g., memory bandwidth bottlenecks, power envelopes, KV-cache management, NPU/GPU acceleration).