Peter Jung
Director Of Paid User Acquisition & Creatives
Prague, Prague, United States
Summary
👤
Senior🎓
Top SchoolPeter Jung is a growth-focused marketing leader and founder with 10 years of experience scaling user acquisition and creative strategies across mobile and digital channels. As Director of Paid User Acquisition & Creatives at App Guardians, he has managed over $33M in ad spend to drive ROI-driven growth for mobile-first clients. He builds at the intersection of marketing and technology—founding Sync Sonic AI to automate lead generation with conversational AI callers and texting agents for instant follow-up. His hands-on technical chops extend to machine learning contributions, including differentiable tensor ops and RNN tests for the Swift for TensorFlow project, reflecting a rare blend of marketing instinct and ML engineering. Based in Prague with global market experience and dual degrees in Advertising and International Relations, he combines creative strategy with product-minded systems thinking. Colleagues describe him as an "AI Ninja" who turns complex automation into practical revenue engines.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Arts (B.A.), International Relations (Global Business), Bachelor of Arts (B.A.), International Relations (Global Business) at University of Southern CaliforniaBachelor of Science (B.S.), Advertising, Bachelor of Science (B.S.), Advertising at University of Illinois Urbana-Champaign
Korean, Spanish, English
Github Skills (9)
automatic-differentiation10
swift10
deeplearning-ai10
rnn-model10
deep-learning10
tensorflow10
n10
testing9
machine-learning9
Programming languages (11)
Github contributions (5)
tensorflow/swift-apis
Nov 2019 - Oct 2020
Swift for TensorFlow Deep Learning Library
Role in this project:
ML Engineer
ML EngineerContributions:15 reviews, 7 commits, 10 PRs in 11 months
Contributions summary:Peter's primary contribution centers around enhancing the Swift for TensorFlow deep learning library. They implemented and tested differentiable versions of core tensor operations like `product` and `reversed`, enabling automatic differentiation within the library. Furthermore, the user added and refined tests for recurrent neural network (RNN) layers, including tests for GRU and bidirectional RNNs with various merge modes, using reference implementations. Their work significantly expanded the library's capabilities for training and differentiating deep learning models written in Swift.
deep-learningswift-for-tensorflowdifferentiable-programmingtensorflowswift
gnosis/prediction-market-agent
Jan 2024 - Apr 2026
Contributions:666 reviews, 603 PRs, 1029 pushes in 2 years 3 months
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