About
Machine Learning Engineer specializing in large-scale LLM systems, distributed training, and production deployment, based in Toronto, Canada.
Ph.D. in NLP from Western University. Experienced in building end-to-end transformer training pipelines, retrieval systems, and production-ready ML infrastructure, with a focus on optimizing model quality and system efficiency under real-world compute constraints.
Experience
Huawei Noah’s Ark Lab — Machine Learning Engineer
Led development and optimization of large-scale LLM training and evaluation pipelines, including a 400M-parameter Transformer trained on 40B tokens across 8×H100 GPUs.
Focused on distributed training, system profiling, and training stability, improving reproducibility, sustained multi-GPU efficiency, and deployment-aware model evaluation.
Western University — Graduate Research Assistant
Built production-scale semantic retrieval systems over 500K+ documents and 1.7M passages, replacing expensive reranking pipelines with FAISS-based ANN retrieval in a unified embedding space.
Combined research and engineering across retrieval, efficiency, and system design, improving end-to-end latency by more than 5× while strengthening result quality and scalability.
Featured Project
CineSeek: Agent-Enhanced Semantic Movie Search
CineSeek is an agent-enhanced semantic movie search system designed for interactive natural-language retrieval. It combines LLM-based query understanding and rewriting, FAISS-based ANN retrieval, and agent-guided reranking and explanation to improve search quality for complex user intents.
The project reflects how I build production-oriented GenAI systems: using agents to improve query understanding and interpretability, while keeping retrieval and serving fast, scalable, and grounded in system-level efficiency.
Technical Focus
Research & Publications
First-author publications in TACL, NAACL Findings, AAAI, and Expert Systems with Applications, focusing on building practical and scalable ML systems.
- TACL 2024: Source-Free Domain Adaptation for Question Answering with Masked Self-training
- NAACL Findings 2024: Source-Free Unsupervised Domain Adaptation for Question Answering via Prompt-Assisted Self-learning
- AAAI 2025: MABR: Multilayer Adversarial Bias Removal Without Prior Bias Knowledge
- Expert Systems with Applications 2024: A Fast Local Citation Recommendation Algorithm Scalable to Multi-topics
- Full list available on Google Scholar.
Contact
Email: [email protected]
Location: Toronto, ON, Canada