Andrej Karpathy
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrej Karpathy is a machine learning leader and researcher with 15 years of experience building deep learning systems that power real-world products and teaching at scale. He led Tesla’s computer vision team for Autopilot, driving data collection, model training, and deployment on custom hardware toward full self-driving, and was an early research scientist at OpenAI working on generative models and RL. A prolific open-source contributor, he created and refined minimal, educational implementations of transformers and auto-diff (nanoGPT, minGPT, micrograd, char-rnn) that are widely used to teach and prototype modern ML ideas. Comfortable spanning research, production engineering, and developer tools, he pairs deep theoretical understanding with pragmatic optimizations like flash attention, bfloat16 support, and C inference implementations. Based in San Francisco, he also helped popularize deep learning education through Stanford’s CS231n and maintains an influential technical blog that distills complex ideas into accessible demos.
15 years of coding experience
7 years of employment as a software developer
MSc, Computer Science, MSc, Computer Science at The University of British ColumbiaBSc, Computer Science, Physics, BSc, Computer Science, Physics at University of TorontoPhD, Computer Science, PhD, Computer Science at Stanford University
Stackoverflow
1,393reputation
171kreached
13answers
10questions
pdf
top-5%
Github Skills (128)
benchmark10
batch-normalization10
python10
image-processing10
attention-mechanism10
c1110
c1710
lua10
data-loading10
deep-learning10
image-generation10
trainings10
self-attention10
search-api10
multihead-attention10
Programming languages (11)
Github contributions (5)
karpathy/char-rnn
May 2015 - Apr 2016
Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch
Role in this project:
ML Engineer
ML EngineerContributions:46 commits, 28 PRs, 52 pushes in 11 months
Contributions summary:Andrej primarily focused on improving the character-level language model. Their work included correcting a bug related to bits-per-character calculation and simplifying code, resulting in better model performance. They also tweaked hyperparameters, such as batch size and learning rate decay, and refined the sampling script for text generation, adding CPU fallback and verbose options. Further, the user added functionality to initialize model parameters from a checkpoint, which can be used for resuming training.
multi-layernatural-language-processingrnnlstm-neural-networksrecurrent-neural-networks
karpathy/ulogme
May 2014 - May 2016
Automatically collect and visualize usage statistics in Ubuntu/OSX environments.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:50 commits, 5 PRs, 4 pushes in 2 years
Contributions summary:Andrej primarily contributed to the front-end development of the project, adding visualizations and enhancing the user interface. They implemented features such as the ability to click on events for more information and added a new overview page. Furthermore, the user worked on bug fixes, added a blog feature, and made general improvements to the existing features. They also worked on integrating the backend by implementing POST requests to add notes to the server.
jscollectstatisticsosxvisualize
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