Research Engineer - Large Language Model & AI Innovation

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

About the team:
The Human-Machine Interaction Lab unites global talents to redefine the relationship between humans and technology. Focused on innovation and user-centered design, the lab strives to advance human-computer interaction research. Our team includes researchers, engineers, and designers collaborating across disciplines to develop novel interactive systems, sensing technologies, wearable and IoT systems, human factors, computer vision, and multimodal interfaces. Through high-impact products and cutting-edge research, we aim to enhance user experiences and interactions with technology.

About the job:

• Design and implement novel AI architectures (e.g., LLMs, transformer-based models) tailored for human-computer interaction scenarios such as conversational agents, adaptive interfaces, or context-aware systems.

• Experiment with state-of-the-art techniques in NLP, reinforcement learning, and multimodal fusion to enhance model performance and user interaction efficiency.

• Optimize LLMs for real-world constraints (e.g., latency, memory footprint) while maintaining high accuracy.

• Conduct end-to-end research from idea prototyping to proof-of-concept validation for AI-driven interaction technologies.

• Analyze product requirements and stakeholder needs while monitoring cutting-edge advancements in AI/ML/NLP to strategically shape research priorities that align technical innovations with user-centric HMI application.

• Present findings internally and contribute to patents where applicable.

Requirements

About the ideal candidate:

• PhD or Master's in Computer Science, Artificial Intelligence, Machine Learning, or a related field.

• Research/development experience in AI, with expertise in deep learning/NLP and LLMs.

• Expertise in designing and training large-scale models (e.g., transformers, reinforcement learning agents) using PyTorch/TensorFlow frameworks.

• Strong proficiency in Python and algorithm problem solving.

• Experience with model compression, quantization, or inference optimization.

• Ability to translate academic research into practical technical solutions.