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).
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.