Vlad Getselevich
Senior Conversational AI Researcher
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolVlad Getselevich is a Senior Conversational AI Researcher at NVIDIA with over a decade of hands-on experience building production-grade NLU and speech systems. He combines deep research expertise in natural language understanding and speech recognition with entrepreneurial instincts from cofounding and serving as CTO of multiple startups that shipped large-scale conversational products and amassed millions of users. At Roku he led the NLU powering the voice assistant on millions of TVs, and at Robin Labs he developed core server-side NLU algorithms trained on one of the largest private conversational datasets. His recent open-source contributions to NVIDIA NeMo show a pragmatic focus on data engineering, error analysis, and maintainable code for intent and slot inference pipelines. With an M.Sc. from Technion and a track record of moving models from research into real-world voice assistants, he blends rigorous technical depth with product-minded delivery.
11 years of coding experience
14 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Technion - Israel Institute of Technology
English, Hebrew, Russian, Spanish
Github Skills (10)
neural-network10
data-preprocessing10
machine-learning10
nlp10
deep-learning10
large-language-models10
python10
generative-ai10
tensorflow9
pytorch9
Programming languages (2)
Github contributions (5)
NVIDIA/NeMo
Apr 2020 - Mar 2021
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
Role in this project:
ML Engineer
ML EngineerContributions:39 reviews, 29 commits, 40 PRs in 11 months
Contributions summary:Vlad's commits primarily focus on adapting a new dataset to the Nemo format and incorporating error analysis for intent and slot inference within the NVIDIA Nemo framework. They implemented the `process_assistant` function within the `examples/nlp/intent_detection_slot_tagging` directory, demonstrating expertise in data preprocessing and transformation for NLP tasks. The contributions involve refactoring code, creating utility files for evaluation, and minor variable renaming, indicating a focus on code maintainability and organization within the Nemo framework.
asrspeech-recognitionnatural-language-processingttsspeaker-diarization
vladgets/NeMo
Mar 2020 - Jun 2020
Neural Modules: a toolkit for conversational AI
Contributions:11 pushes, 5 branches in 3 months
nlpconversationalconversational-aimachine-learningchatbot
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