Ilya Eder
Principal Machine Learning Engineer at Inworld AI
Potsdam, Brandenburg, Germany
Summary
👤
Senior🎓
Top SchoolIlya Eder is a Principal Machine Learning Engineer based in Potsdam with over a decade of hands-on experience building production-grade AI systems and leading engineering teams. He has driven core speech and voice technologies at Yandex that scaled to tens of millions of daily users, pioneered neural approaches to ASR, and later architected privacy-first, personalizable conversational AI for constrained environments. As Head of ML/CTO at i2x and in stealth deep-tech leadership roles, he combined research-grade modeling with pragmatic full-stack ML product development for enterprise customers like Vodafone and Siemens. Ilya is an active open-source contributor to foundational projects such as Kaldi—improving RNNLM components and performance-critical functions—bridging research, systems, and engineering. He holds a PhD in Psychophysiology/Computational Neuroscience and applies this multidisciplinary background to problems from protein–ligand interaction modeling to conversational agents. Known for shipping end-to-end AI products, he blends deep research insight with a talent for building and mentoring cross-functional teams.
11 years of coding experience
7 years of employment as a software developer
Bioinformatics, Bioinformatics at Yandex Data Analysis SchoolLomonosov Moscow State University
English, German, Spanish, Russian, Portuguese, French
Github Skills (7)
kaldi10
machine-learning10
speech-recognition10
c-language10
cprogramming-language10
shell5
cuda5
Programming languages (7)
Github contributions (5)
kaldi-asr/kaldi
Aug 2015 - Sep 2015
kaldi-asr/kaldi is the official location of the Kaldi project.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:6 comments in 20 days
Contributions summary:Ilya contributed to the Kaldi project by implementing and modifying components related to Recurrent Neural Network Language Models (RNNLM), specifically the RNNLM-HS implementation. Their work involved bug fixes, performance optimization, and integrating the maxent component into the RNNLM. They also worked on improving the configuration and testing of the exponential function (expf). This suggests a focus on enhancing the core functionalities of the speech recognition toolkit.
cudakaldiasrspeech-to-textkaldi-asr
ilya-edrenkin/tensorflow
Apr 2016 - Sep 2017
Contributions:1 PR, 4 pushes, 2 branches in 1 year 5 months
computationscalabledata-sciencemachine-learninggraphs
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