Braden Hancock
Research Partner at Gusto
Redwood City, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBraden Hancock is an AI researcher-founder and investor with 11 years of experience turning academic ML research into large-scale products and businesses. He co-founded Snorkel AI, led its technology and applied research teams to productize weak supervision and data-centric ML (raising $235M and achieving eight-figure ARR), and later ran GenAI evaluation at Meta for the Llama 3.x family. Now a Research Partner at Laude (nonprofit and venture) and an advisor to startups like Gusto and Martian, he focuses on helping research translate into impact at billion-dollar scale. His work blends deep Stanford PhD–level expertise in LLMs, weak supervision, and data curation with hands-on open-source contributions to the widely used Snorkel project and its tutorials. An interesting through-line: he consistently moves tools from notebook tutorials to production-grade services that support many frontier LLM builders and Fortune 500 customers.
10 years of coding experience
6 years of employment as a software developer
BS Mechanical Engineering Mathematics Minor, BS Mechanical Engineering Mathematics Minor at Brigham Young UniversityPh.D. Computer Science, Ph.D. Computer Science at Stanford University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
data-manipulation10
machine-learning10
jupyter-notebook10
develop10
nlp10
python10
snorkel10
data-science10
data-analysis9
machine-learning-models9
natural-language-processing8
data-augmentation6
tensorflow5
Programming languages (7)
Github contributions (5)
snorkel-team/snorkel-tutorials
Jul 2019 - Aug 2022
A collection of tutorials for Snorkel
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:3 reviews, 36 commits, 85 PRs in 3 years 1 month
Contributions summary:Braden contributed extensively to the development of a spam detection tutorial within the Snorkel-tutorials repository. Their work involved setting up the initial structure, creating data loading and download scripts, and iteratively refining the tutorial's content. They implemented labeling functions, updated import paths, and added necessary dependencies. Additionally, they made modifications to integrate a multi-task learning section and address comments.
snorkel
snorkel-team/snorkel
Jun 2016 - Aug 2021
A system for quickly generating training data with weak supervision
Role in this project:Data Scientist
Data ScientistContributions:2 releases, 36 reviews, 172 commits in 5 years 2 months
Contributions summary:Braden's commits focus on modifying a tutorial notebook to import necessary libraries, including `defaultdict` and `shuffle`. The changes introduce and utilize these tools in the `tutorial/CDR_Tutorial.ipynb` notebook, indicating a contribution to the notebook's functionality. These modifications suggest a focus on data manipulation or preparation, likely in the context of a machine-learning task related to the "snorkel" project.
weak-supervisionpythondata-sciencemachine-learninglabeling
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