Daniel Johnson
Member Of Technical Staff at Transluce
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Johnson is a research-focused machine learning engineer with 11 years of experience building interpretable, robust, and scalable ML systems from research prototypes to production. He held research roles at Google (including AI Resident and Research Scientist) and now works as a Member of Technical Staff at Transluce in San Francisco, blending deep probabilistic and systems expertise. His open-source contributions to high-profile projects like JAX and TensorFlow Probability emphasize probabilistic reasoning, advanced indexing, batching rules, and numerics—work that directly improves reliability and performance for large-scale ML. Daniel also implemented core layers and tooling for Google Research projects (GFSA, Dex) and has a history of applied perception and generative-model work from Cruise and earlier music-composition research. He holds a PhD-level background in computer science from the University of Toronto and a dual undergraduate degree in math and CS from Harvey Mudd, enabling him to bridge formal theory with practical engineering. Colleagues would note his knack for eliminating nondeterminism and thoughtful developer-facing fixes that make complex ML systems more predictable and debuggable.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of TorontoBachelor’s Degree Mathematics and Computer Science, Bachelor’s Degree Mathematics and Computer Science at Harvey Mudd College
Github Skills (28)
python10
machine-learning10
recurrent-neural-networks10
numpy10
lstm10
language-design10
ai10
neural-network10
jax10
type-theory10
haskell10
theano10
testing9
music-generation9
graph-algorithms9
Programming languages (8)
Github contributions (5)
danieldjohnson/biaxial-rnn-music-composition
Jul 2015 - May 2017
A recurrent neural network designed to generate classical music.
Role in this project:
ML Engineer
ML EngineerContributions:15 commits, 3 PRs, 13 pushes in 1 year 9 months
Contributions summary:Daniel primarily contributed to the development of a recurrent neural network for music composition. Their commits focused on adjusting and improving the model's architecture, including the addition of dropout layers for regularization and handling bitwidth issues. They implemented changes to the data loading and preprocessing, and optimized the training procedure. These changes suggest the user was heavily involved in model refinement and ensuring proper functionality.
machine-learningclassical-musicrecurrent-neural-networkneural-networkmusic
google-research/dex-lang
Jun 2020 - Sep 2021
Research language for array processing in the Haskell/ML family
Role in this project:Back-end Developer
Back-end DeveloperContributions:109 reviews, 84 commits, 18 PRs in 1 year 3 months
Contributions summary:Daniel contributed to the development of the `google-research/dex-lang` repository by implementing syntactic sugar and core language features. Their work involved adding `fromOrdinal` and other index-related functions to the prelude and modifying the parser to support implicit lambda syntax. The user also refactored negative literal parsing and implemented the first pass at record/variant and the simplification for them. Additionally, they made several modifications to the parsing and printing mechanisms, including adding operators for function and isomorphism composition.
arrayhomotopy-type-theoryautomatic-differentiationhaskellarray-processing
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