Virginia Adams
Senior Research Staff Member at NVIDIA
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolVirginia Adams is a Senior Research Staff Member and deep learning applied scientist with five years of focused experience building and aligning large language models for production at NVIDIA and inference optimization at Together AI. She blends hands-on ML engineering—contributing to notable open-source projects like NVIDIA/NeMo, including medical-domain entity linking and prompt tuning—with academic research at Stanford SAIL and CRFM on dialogue, NLG, and human-AI interaction. Her work emphasizes reward modeling and parameter-efficient tuning to make LLMs more reliable and deployable in real-world systems. An MIT CS graduate and Critical Language Scholar, she brings uncommon cross-cultural fluency in Chinese to projects involving multilingual data and evaluation. Colleagues rely on her ability to move ideas from prototype notebooks to scalable back-end implementations that ship in product environments.
5 years of coding experience
7 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at Massachusetts Institute of TechnologyMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Stanford University
English, Chinese, Spanish
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
pytorch10
machine-learning10
nlp10
deep-learning10
large-language-models10
python10
generative-ai9
Programming languages (2)
Github contributions (5)
NVIDIA/NeMo
Apr 2021 - Jan 2023
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:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:136 reviews, 149 commits, 104 PRs in 1 year 8 months
Contributions summary:Virginia made several contributions focused on entity linking within a medical-domain-focused AI framework. Their work included modifying data preprocessing scripts for machine translation, adding new datasets related to entity linking, and integrating entity linking encoder models. They also contributed to the development of entity linking demonstration notebooks. Furthermore, the user worked on prompt tuning implementation, indicating involvement in model training and optimization.
asrspeech-recognitionnatural-language-processingttsspeaker-diarization
vadam5/NeMo
Mar 2021 - Nov 2021
NeMo: a toolkit for conversational AI
Contributions:70 pushes, 2 branches in 7 months
nlpconversationalconversational-aichatbotnemo
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