Guillaume Klein is a Senior Staff Machine Learning Engineer with 11 years of experience, currently optimizing large language model serving and inference at Apple. He authored and led the OpenNMT ecosystem and created CTranslate2, a widely used high-performance inference engine that made Transformer models cheaper and more practical to run in production. His background spans research and engineering—building training and serving backends, speech recognition servers, and low-level C++ optimizations for numerical stability and vectorized performance. An active open-source maintainer, he has contributed to TensorFlow Addons and numerous ML repos, balancing API design, compatibility fixes, and performance engineering. Based in Paris, he combines deep NLP research roots with hands-on systems work that bridges model development and efficient deployment.
11 years of coding experience
10 years of employment as a software developer
Engineer's degree, Computer Engineering, Engineer's degree, Computer Engineering at EPITA: Ecole d'Ingénieurs en Informatique
Contributions:7 releases, 33 reviews, 56 commits in 1 month
Contributions summary:Guillaume primarily contributed to the `faster-whisper` project by implementing and refining features related to the Whisper model's transcription capabilities. Their work includes adding functionalities such as initial prompt and prefix parameters, as well as parameters to control word timestamps and suppress certain tokens. The user also improved the error handling and model compatibility and updated the CTranslate2 version.
Open Source Neural Machine Translation in Torch (deprecated)
Role in this project:
Backend Developer
Contributions:873 commits, 170 PRs, 757 pushes in 2 years 8 months
Contributions summary:Guillaume primarily contributed to the development of a sequence-to-sequence neural machine translation model, as evidenced by modifications to the core training loop. Their work involved adjustments to the LSTM implementation and attention mechanism, along with the integration of a generator module. They also contributed to the development of the vocabulary and input data preparation using the Lua language and Torch library.
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