Si Yuan Lee — Software Engineer, ML InfrastructureSi Yuan Lee

Part of AML frameworks team. Focused on ML infra which is the core part of recommendation, ads and search direction. Optimised Sparse MoE by +23.3%, increasing throughput of 8B fine ranking model by +15.36%. Optimised flash_attn_varlen_backward, increasing throughput by +4.84% for the same model.

Part of Facebook Reels AI Foundation Team. Form the core AI logic of large-scale recommendation. Dabbled with ML training and experiments for different expert and foundational models. Improved Attention Kernels (fwd and bwd sdpa kernels) by > 33% resulting in training improvement > 5% qps. Implemented blockwise optimisation of SwigLU on MoE, resulting in 30% efficiency gain for SwigLU MoE, improving inference qps of 16B OneFlow model by 20% from 1490 QPS to 1790 QPS.

Built an Agentic RAG tool that automated 30% of SQL query construction for post-trade regulatory reporting. Remodelled the LLM Suite API with FastAPI, driving adoption across 5 global teams, and engineered metadata-driven vector DB switching that cut p95 retrieval latency from 6s to 2s.

Designed a PEFT fine-tuning pipeline on AWS EC2 for TinyLlama, StabilityAI, and Qwen models. Achieved a perplexity drop from 16 to 4 for Java Card code completion, with the fine-tuned model passing 90% of regex test cases versus 0% at baseline.