Lukas Biewald
General Manager Of Weights And Biases at CoreWeave
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolLukas Biewald is a seasoned AI entrepreneur and engineering leader with over a decade building developer tooling and ML platforms, best known for founding Weights & Biases and now leading its team as General Manager at CoreWeave. He combines hands-on ML engineering—demonstrated by practical open-source work on wandb integrations and educational ML repos—with strategic product and go-to-market leadership from prior exits like Figure Eight. A Stanford-trained computer scientist and mathematician, Lukas has repeatedly turned research-grade ML into widely adopted developer tools and has an active angel portfolio in cutting-edge AI and infrastructure startups. Less obvious: his contributions span low-level framework integrations (TensorFlow hooks) to pedagogical projects (ml-class), signaling both deep technical fluency and a commitment to growing the AI practitioner community.
11 years of coding experience
24 years of employment as a software developer
MS Computer Science, MS Computer Science at Stanford University
Stackoverflow
117reputation
0reached
2answers
0questions
Github Skills (27)
pytorch10
python10
machine-learning10
rnn-model10
n10
text-classification10
mask-rcnn10
weighting10
sentiment-analysis10
keras10
lstm10
tensorflow10
deep-learning10
natural-language-processing10
faster-rcnn10
Programming languages (5)
Github contributions (5)
lukas/ml-class
Sep 2015 - Sep 2020
Machine learning lessons and teaching projects designed for engineers
Role in this project:
ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:345 commits, 24 PRs, 263 pushes in 5 years
Contributions summary:Lukas's commits primarily involve the development and implementation of machine learning models for text classification. They focused on training and evaluating models using Keras for sentiment analysis, exploring different architectures like CNNs and RNNs (LSTMs). Furthermore, they investigated techniques such as hyperparameter optimization using grid search and incorporating attention mechanisms to improve model performance. They also included examples of audio generation, demonstrating a multi-faceted understanding of machine learning tasks.
machine-learningmachine-learning-tutorialsdeep-learning-tutorials
wandb/examples
Jan 2018 - Jul 2020
Example deep learning projects that use wandb's features.
Role in this project:ML Engineer
ML EngineerContributions:51 commits, 6 PRs, 35 pushes in 2 years 6 months
Contributions summary:Lukas contributed multiple examples showcasing the integration of machine learning models with the Weights & Biases platform. Their work involved implementing and training CNNs on the Fashion MNIST dataset and the CIFAR-10 dataset using Keras and PyTorch, demonstrating proficiency in model development and experiment tracking. The user also provided a TensorFlow estimator example for MNIST. Furthermore, they integrated XGBoost for a dermatology classification task and explored a scikit-learn example.
deep-learningwandb
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