Joachim Fainberg is a co-founder and CTO with 11 years of experience building applied AI systems, currently leading The Forecasting Company from Paris to turn probabilistic models into production forecasting products. His background blends deep academic training (PhD and MSc in Artificial Intelligence from the University of Edinburgh) with hands-on ML engineering in industry roles at Vind AI, JPMorgan Chase, and Sonos. Joachim has practical expertise in NLP and ASR, including notable contributions to the widely used open-source Kaldi speech toolkit, where he improved robustness and model-conversion tooling. He pairs technical leadership with product focus—moving research into scalable, reliable systems—and has a rare cross-disciplinary past spanning music/tonmeister training and technical theatre engineering that informs a pragmatic, systems-minded approach. Fluent in building teams and code, he is equally comfortable fixing low-level model bugs and shaping company-level ML strategy.
11 years of coding experience
12 years of employment as a software developer
Single module Fitness, Single module Fitness at Norwegian School of Sport Sciences (NIH)
Doctor of Philosophy (PhD) Artificial Intelligence, Doctor of Philosophy (PhD) Artificial Intelligence at The University of Edinburgh
Single modules Calculus Linear Algebra Electromagnetism Psychology, Single modules Calculus Linear Algebra Electromagnetism Psychology at University of Oslo
Diploma Film, Diploma Film at New York Film Academy
Diploma Italian, Diploma Italian at Scuola da Vinci Firenze
BMus (Hons) Music & Sound Recording BMus (Tonmeister), BMus (Hons) Music & Sound Recording BMus (Tonmeister) at University of Surrey
kaldi-asr/kaldi is the official location of the Kaldi project.
Role in this project:
ML Engineer
Contributions:12 PRs, 27 comments in 4 years
Contributions summary:Joachim made several contributions to the Kaldi project, primarily focused on the development and improvement of the speech recognition and related components. These changes included fixing errors, such as those related to incorrect variable usage within the nnet3 framework. The user also worked on adapting scripts for improved functionality, including updates to data processing scripts, and the addition of new scripts such as converting nnet2 models to nnet3 models. The modifications suggest a deep understanding of the project's architecture and a focus on enhancing its robustness and efficiency.
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.