Jagadeesh Balam
Sr. Applied Research Manager at NVIDIA
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolJagadeesh Balam is a Senior Applied Research Manager and AI scientist in the San Francisco Bay Area with a PhD and over a decade of experience building speech recognition and NLU systems for companies like NVIDIA, Target, and Nuance. He leads development of state-of-the-art deep learning models for NVIDIA’s open-source NeMo toolkit and has a track record of turning research prototypes into production ML pipelines that serve real user-facing products. His background spans acoustic and language modeling, decoder work for large-vocabulary ASR, and practical engineering from DSP to production ML, blending hands-on experimentation with product-focused delivery. Jagadeesh excels at cross-functional collaboration with product, business, and engineering teams and is known for being curious, pragmatic, and solution-oriented. He also contributes to major open-source ASR documentation—improving usability for NVIDIA/NeMo users—demonstrating attention to developer experience as well as model performance.
6 years of coding experience
13 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceMS/PhD, Electrical and Computer Engineering, MS/PhD, Electrical and Computer Engineering at University of California, Santa Barbara
Github Skills (7)
asr10
documentation10
generative-ai9
large-language-models8
python7
deep-learning7
sphinx6
Programming languages (2)
Github contributions (5)
NVIDIA/NeMo
May 2020 - Nov 2022
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:
Technical Writer & Documentation Specialist
Technical Writer & Documentation SpecialistContributions:112 reviews, 59 commits, 34 PRs in 2 years 6 months
Contributions summary:Jagadeesh primarily focused on improving the documentation for the `nvidia/nemo` repository, specifically for the ASR (Automatic Speech Recognition) module. They updated various documentation files, including those related to 8 kHz models, QuartzNet, and Jasper, restructuring sections, adding references, and providing links to resources. The user also added a script for converting audio files to G.711 format and created a new tutorial on streaming ASR. These contributions significantly enhance the usability and clarity of the documentation.
asrspeech-recognitionnatural-language-processingttsspeaker-diarization
findkim/NeMo
Aug 2022 - Sep 2022
NeMo: a toolkit for conversational AI
Contributions:2 pushes in 6 days
nlpconversationalconversational-aidialogue-systemstopic-modeling
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