Julien Balian is a Senior Machine Learning Engineer based in Greater Paris with 7 years of experience building production-ready speech and audio ML systems for embedded devices and cloud services. He has led speaker recognition efforts and improved acoustic and keyword-spotting models at Sonos, bringing research-grade approaches (metric learning, prototypical scoring, clustering) to Raspberry Pi–class hardware. Julien founded a speech-analysis SaaS, designing end-to-end pipelines from data collection and large-scale training to BI dashboards and microservice orchestration, showing both product and engineering ownership. His background spans speech recognition, diarization, signal processing and NLP, with hands-on implementation in PyTorch, TensorFlow, Rust and scalable tooling on AWS. An active contributor to the tiny inference library tract, he has fixed low-level operator bugs and added support for new ops and data types—highlighting his ability to work across model research and inference-runtime details. With an MSc in Software Engineering (Distinction) and a track record of turning academic methods into deployable systems, he excels at squeezing ML performance onto constrained devices.
7 years of coding experience
6 years of employment as a software developer
4th year - Master's degree, Computer Science, 4th year - Master's degree, Computer Science at SUPINFO - The International Institute of Information Technology
Master of Science (MSc), Computer Software Engineering, with Distinction, Master of Science (MSc), Computer Software Engineering, with Distinction at Oxford Brookes University
Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
Role in this project:
Backend Developer
Contributions:1 review, 23 commits, 28 PRs in 9 months
Contributions summary:Julien primarily focused on implementing and refining features within the `tract` library. Their contributions involved fixing bugs related to the `tile` and `unstack` operators, including serialization and handling of stride. They also added new operators like `unstack` and `stack`, and incorporated type conversions. Furthermore, the user made improvements to the core logic and added support for new data types.
Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
Contributions:90 pushes, 20 branches in 1 year 8 months
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Julien Balian - Senior Machine Learning Engineer at Sonos, Inc.