Christopher Fox
Software Engineer at Datadog
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolChristopher Fox is a software engineer and machine learning practitioner with 7 years of experience building and shipping NLP and deep learning systems, now focused on LLM observability at Datadog. He previously led ML efforts at ASAPP, productionizing multiple PyTorch models for real-time agent assistance and spearheading an LLM-based knowledge‑base QA feature while mentoring a team of MLEs. Earlier roles at Clarifai and internships at Google and Apple bring solid full‑stack and systems experience—Go APIs, Python/Django services, and scalable data pipelines involving Redis, Postgres and Redshift. Christopher is an active open-source contributor to ML tooling (notably improving the SRU recurrent unit implementation used to train RNNs as fast as CNNs), reflecting a penchant for making research code more usable in production. He combines applied research roots from an MS in Computer Science with pragmatic engineering that bridges models, infrastructure, and observability.
7 years of coding experience
12 years of employment as a software developer
Bachelor of Science (BS) Math Economics, Bachelor of Science (BS) Math Economics at University of WashingtonMaster of Science (MS) Computer Science, Master of Science (MS) Computer Science at North Carolina State University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
pytorch10
deeplearning-ai10
recurrent-neural-networks10
nlp10
deep-learning10
python9
setuptools8
py8
version-control7
git7
Programming languages (3)
Github contributions (5)
asappresearch/sru
Aug 2018 - Oct 2018
Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 7 commits, 2 pushes in 1 month
Contributions summary:Christopher contributed to the maintenance and improvement of the SRU (Simple Recurrent Unit) implementation. Their commits include making installation requirements optional, correcting author information, updating the paper title and package description, and fixing image URLs. Furthermore, the user updated the package version and added a package docstring. These changes suggest an active role in refining the usability and documentation of the SRU library.
nlppytorchgruarxivabs
ASAPPinc/fastText
Sep 2018 - Oct 2018
Library for fast text representation and classification (with ASAPP minor fixes)
Contributions:3 PRs, 1 push in 1 month
nlpminorrepresentationfixeslda
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