Jeff Raubitschek
Software Engineer at Weights & Biases
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJeff Raubitschek is a software engineer with seven years of recent experience based in San Francisco and a long track record of technical roles stretching back to Intel and Google. He currently develops developer-facing tooling and example ML projects at Weights & Biases, contributing practical TensorFlow demos and sweep/validation notebooks that help teams reproduce and iterate experiments. As founder of Splenvid and an engineer at Beep Networks, he blends product-minded entrepreneurship with hands-on implementation across startups and established companies. His background includes staff-level engineering and tech lead/manager responsibilities at Google, giving him experience in both individual contribution and team leadership. Jeff holds a BS in Computer Engineering from Virginia Tech and brings a mix of systems-level rigor and ML tooling fluency that makes him effective at translating research workflows into reliable engineering. An under-the-radar strength is his habit of improving documentation and example code to accelerate adoption of complex tools.
7 years of coding experience
14 years of employment as a software developer
BS, Computer Engineering, BS, Computer Engineering at Virginia Tech
Stackoverflow
171reputation
4kreached
2answers
0questions
Github Skills (8)
keras10
machine-learning10
deep-learning10
tensorflow10
python10
wandb10
numpy6
k-fold6
Programming languages (11)
Github contributions (5)
wandb/examples
May 2019 - Feb 2022
Example deep learning projects that use wandb's features.
Role in this project:
ML Engineer
ML EngineerContributions:11 reviews, 61 commits, 15 PRs in 2 years 10 months
Contributions summary:Jeff primarily contributes to example deep learning projects. Their work includes modifying and adding features to existing TensorFlow-based example, such as adding a max_steps flag and refactoring. The user also adds a new demo for using wandb sweeps with a Python notebook, demonstrating experiment configuration. They add a demo for validation data.
pytorchdeep-learningdeep-learning-examplemachine-learningwandb
wandb/yea-wandb
Jul 2021 - Aug 2023
Contributions:22 reviews, 77 PRs, 164 pushes in 2 years 1 month
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