Senior Machine Learning Engineer @ Amazon AGI
Scaling multimodal foundation models — optimizing how they learn, generalize, and align through data and systems co-design.

I work at the intersection of machine learning and distributed systems,
designing large-scale learning pipelines and multimodal data systems that improve how foundation models learn from vast, diverse signals.

My focus areas:

• 🧠 Training dynamics & optimization — improving convergence, stability, and efficiency of large-scale multimodal models
• 🧩 Learning-centric systems — integrating data, architecture, and feedback to enhance representation learning and model alignment
• ⚙️ Scalable orchestration — leveraging Ray, Spark, and Kubernetes to parallelize multimodal workloads across thousands of GPUs
• 🔍 Evaluation & feedback loops — automating model-driven data refinement and continual quality signals for alignment and adaptation

My work centers on how models learn, not just how they’re trained.

1. Models and systems co-evolve.
The best architectures emerge when data, compute, and learning dynamics are designed together.

2. Scale reveals behavior.
Many learning problems only appear — and can only be solved — at massive scale.

3. Data is part of the model.
Every batch defines what the model becomes.

“At scale, learning is a systems problem — and every system is a hypothesis about how intelligence forms.”
— Duo An