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

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:

• Design and build scalable infrastructure to support Reinforcement Learning, Online Search, Recommendation Systems, large model fine-tuning and evaluation/deployment.

• Develop efficient ML solutions for Recommendation Systems and RL problems, including Multi-Armed and Contextual Bandit, Tree Search, and Multi-Agent system orchestration.

• Implement and optimize deep learning architectures, including custom Transformers for agentic and decision-making systems.

• Apply search and optimization techniques to efficiently fine-tune RL and ML models.

• Work with large multimodal models (LLMs, VLMs), analyze their components, and fine-tune them for task-specific applications.

• Conduct systematic benchmarking, new papers reading, experimentation, and validation in both simulation and real-world product environments.

• Collaborate closely with research teams to scale online RL training capabilities and improve system robustness and accuracy.

• Explore and integrate emerging AI methodologies and tools into production platforms.

About the ideal candidate:

• Master’s or PhD in Computer Science, Machine Learning, or a related field.

• Excellent Python programming skills with strong software engineering practices.

• Strong foundation in Reinforcement Learning, Deep Learning, Recommender Systems, and Transformer-based architectures.

• Demonstrated experience implementing RL algorithms beyond academic prototypes.

• Hands-on experience with PyTorch and distributed training frameworks such as DeepSpeed.

• Proven research excellence, including at least one publication in top-tier venues (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA, RLC).

• Familiarity with LLM post-training techniques such as RLHF, PPO/GRPO, SFT, LoRA, or MoE is a strong asset.

• Experience with multi-agent RL systems or tool-use agents is an advantage.