Bilal Khan is a research engineer with nine years of experience building and optimizing large-scale ML training and inference systems, currently at Isomorphic Labs. He has driven practical improvements across PyTorch and Hugging Face Transformers—enabling robust checkpointing and optimizer scheduling that streamline fine-tuning workflows—and has interned on core performance teams at NVIDIA and Databricks working on GPU memory, kernel autotuning, and multi-thousand-GPU training runs. His background includes large-scale optimizer research with Google Brain and hands-on LLM training and inference engineering at Cohere, giving him deep fluency in CUDA, NCCL, distributed parallelism, and productionizing model stacks. Based in Canada and trained at the University of Waterloo, Bilal mixes research rigor with production pragmatism and prefers to be contacted via bilal2vec.com or bilal2vec@gmail.com.
8 years of coding experience
2 years of employment as a software developer
Bachelor of Software Engineering, Bachelor of Software Engineering at University of Waterloo
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Role in this project:
ML Engineer
Contributions:12 commits, 4 PRs, 26 comments in 2 months
Contributions summary:Bilal primarily focused on modifying existing training scripts within the Hugging Face Transformers library. Their contributions centered around enhancing the training process, specifically by enabling the saving and loading of optimizer and scheduler states, and allowing training to resume from saved checkpoints. They also made minor improvements to documentation. These changes streamline the fine-tuning process for various language models within the library.
Contributions:22 PRs, 514 pushes, 28 branches in 1 year 11 months
pythonsciencedata-sciencemachine-learningbowl
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