Adrian Łańcucki
Senior Deep Learning Algorithms Engineer at NVIDIA
Warsaw, Masovian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolAdrian Łańcucki is a Senior Deep Learning Algorithms Engineer based in Warsaw with a decade of experience building and deploying state-of-the-art NLP and speech models. With a PhD in Machine Learning and a strong academic foundation in computer science and mathematics, he blends research rigor with production-focused engineering at NVIDIA since 2019. His open-source contributions to NVIDIA’s DeepLearningExamples—fixing inference bugs in Jasper and improving FastPitch—show hands-on expertise across the full model lifecycle from training to deployment. Adrian’s background includes research stints at NYU and Microsoft and a brief AI researcher role at Pathway, reflecting a career that moves fluidly between prototyping and production. He’s particularly adept at making large language and speech models robust and deployable on enterprise infrastructure, often surfacing practical fixes that improve real-world inference performance.
10 years of coding experience
Student Exchange Programme, Computer Science, Student Exchange Programme, Computer Science at California State University - East BayBachelor of Science (BSc), Computer Science, Bachelor of Science (BSc), Computer Science at University of Wroclaw
Polish, English, French
Github Skills (7)
pytorch10
speech-recognition10
speech-synthesis10
deep-learning10
tensorflow4
nlp3
computer-vision3
Programming languages (3)
Github contributions (5)
NVIDIA/DeepLearningExamples
Jun 2020 - Jan 2023
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Role in this project:
ML Engineer
ML EngineerContributions:5 reviews, 52 commits, 11 PRs in 2 years 7 months
Contributions summary:Adrian primarily contributed to the PyTorch implementation within the `nvidia/deeplearningexamples` repository, focusing on speech recognition and synthesis models. Their work included debugging and fixing inference-related bugs in the Jasper speech recognition model, along with updates to FastPitch, a speech synthesis model. The commits also involved adding and updating scripts and configurations related to model deployment and pre-trained models, demonstrating an ability to work with the whole deep learning lifecycle.
forecastingcaffe2translationspeech-recognitionstate-of-the-art
distsup/DistSup
Jan 2020 - Apr 2020
Representation learning for NLP @ JSALT19
Contributions:5 commits, 9 pushes, 2 branches in 3 months
nlprepresentationbertrepresentation-learningnatural-language-processing
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