Jason Phang
Member Of Technical Staff at EleutherAI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJason Phang is a Member of Technical Staff at OpenAI and Lead Scientist at EleutherAI with 12 years of experience building and evaluating large-scale ML systems. He combines academic rigour as a NYU PhD candidate in Data Science with hands-on engineering—contributing core features to high-profile open-source projects such as Hugging Face Transformers and GPT-NeoX. His work spans model development, optimization for model-parallel and single-GPU deployments, and applied ML in healthcare, including a TensorFlow breast cancer classifier that improved radiologist performance. Jason has interned at Google and Microsoft, bridging research and production while contributing evaluation tools like the lm-evaluation-harness for few-shot benchmarking. Based in San Francisco, he brings a rare mix of deep research, production pragmatism, and open-source stewardship that accelerates both model capabilities and real-world evaluation.
12 years of coding experience
2 years of employment as a software developer
PhD Data Science, PhD Data Science at New York UniversityBachelor of Science (BS) Mathematics Statistics Economics, Bachelor of Science (BS) Mathematics Statistics Economics at University of Chicago
Stackoverflow
36reputation
2kreached
1answer
0questions
Github Skills (30)
transformers10
pytorch10
openai-api10
language-model10
parallelization10
python10
pre-trained-model10
classification10
model-driven10
machine-learning10
inference10
nlpjs10
model-building10
deep-learning10
tensorflow10
Programming languages (6)
Github contributions (5)
nyu-mll/jiant
Mar 2019 - Oct 2022
jiant is an nlp toolkit
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1 release, 71 reviews, 145 commits in 3 years 8 months
Contributions summary:Jason primarily contributed to the inference interface and data processing for a natural language processing (NLP) toolkit. They developed a REPL (read-eval-print loop) and corpus processing capabilities within the `cola_inference.py` file. Further contributions included adding features for evaluating model performance using labeled data. Their work directly aligns with the toolkit's purpose to support NLP tasks such as CoLA.
nlptransformersmultitask-learningsentence-representationbert
nyukat/breast_cancer_classifier
Apr 2019 - Feb 2020
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Role in this project:ML Engineer
ML EngineerContributions:8 commits, 10 pushes, 2 branches in 10 months
Contributions summary:Jason primarily contributed to the implementation and refinement of a deep learning model for breast cancer classification. Their work included fixing issues related to output column swapping and adjusting assertions for variable batch sizes. The user also added a TensorFlow implementation of the model, indicating a focus on model development and potentially deployment. Furthermore, the addition of image-wise models and supporting notebooks suggests contributions to model evaluation and analysis.
breast-cancerbreast-cancer-diagnosistensorflowclassificationscreening
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