I build reliable and verifiable systems for large-scale model development, with a focus on distributed training, correctness, diagnosability, and resilience.

My work spans pretraining, post-training and reinforcement learning, inference and rollout, and agent and data-generation infrastructure. Recent work has supported internal dense and MoE training programs on up to 4,096 GPUs.

Previously, I was a Senior Research Engineer at Microsoft Research Asia. I was a core contributor to nnScaler (OSDI 2024), from the early development of AutoDist through later system development and model-research integrations. I also contributed real-world bug cases, design feedback, and technical support to TrainVerify (SOSP 2025).

My interests include:

• Large-scale distributed training and parallel execution
• Training correctness, diagnosis, and system resilience
• Post-training, RL, inference, and rollout systems
• Agent and data-generation infrastructure
• Model-system co-design

More details and technical writing: zyeric.github.io/cv