Alexandre Tamborrino
Senior Machine Learning Engineer at Spotify
Paris, Ile-de-France
Summary
👤
Senior🎓
Top SchoolAlexandre Tamborrino is a Senior Machine Learning Engineer based in Paris with 13 years of experience building production-grade ML systems and search relevance at scale. He has driven applied NLP and retrieval innovations at Spotify, notably helping introduce natural-language semantic search for podcasts to hundreds of millions of users, and previously led research at Samsung AI Labs on conversational AI, real-time translation and vision. Comfortable across the stack, he combines deep learning (Transformers, LLMs, PyTorch/TensorFlow) with scalable engineering in Scala/Spark and cloud-native deployment. Alexandre contributes to type-safe big-data tooling—adding UDT support to the frameless Spark library—reflecting a blend of pragmatic engineering and strong type-driven design. He’s equally at home prototyping cutting-edge models and hardening them for low-latency, large-scale production environments.
13 years of coding experience
9 years of employment as a software developer
Master of Science (M.S.), Computer science, A, Master of Science (M.S.), Computer science, A at Institut national des Sciences appliquées de LyonMaster of Science (M.S.), Software Engineering of Distributed Systems, A, Master of Science (M.S.), Software Engineering of Distributed Systems, A at KTH Royal Institute of Technology
French, English, Spanish, Swedish
Stackoverflow
478reputation
12kreached
5answers
2questions
Github Skills (11)
functional-programming10
spark10
scala10
machine-learning8
ml8
websocket6
sbt6
playframework6
file-upload6
azure-storage6
loops6
Programming languages (4)
Github contributions (5)
typelevel/frameless
Aug 2017 - Dec 2017
Expressive types for Spark.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:18 commits, 3 PRs, 34 comments in 3 months
Contributions summary:Alexandre's commits focus on adding and supporting User Defined Types (UDTs) within the Frameless library. They introduced UDT support for TypedEncoder, enabling the encoding of custom classes. The commits also involve integrating User Defined Types for Spark ML's Vector and Matrix, along with related testing and examples, demonstrating the user's efforts to extend Frameless's type-safe capabilities within the Spark environment. The contributions include new classes and tests for UDT functionality.
functional-programmingspark-mlexpressivesparkscala
atamborrino/PlayRTC
Oct 2013 - Dec 2013
Contributions:60 commits in 2 months
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