Ricardo Decal
Product Manager at Anyscale
San Francisco, California, United States
Summary
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Rockstar🎓
Top SchoolRicardo Decal is a product manager and former ML engineer with 11 years of experience building full-stack, full life-cycle AI systems and production ML workflows. Currently at Anyscale, he focuses on large-scale distributed AI workloads and the Ray LLM APIs, bringing hands-on knowledge of Ray from contributing bug fixes, docs, and data/runtime improvements to that influential open-source project. His background spans applied ML in ecosystem restoration at Dendra Systems, clinical data science for cardiac care, and neuroscience research using deep RL—demonstrating a rare mix of domain breadth and rigorous experimental methods. He holds a Master’s in Data Science and a machine learning nanodegree, and routinely translates research-grade models into reliable, scalable production services. Outside engineering, Ricardo’s field research and travel writing reflect an adventurous curiosity that informs creative problem solving in product and ML design.
11 years of coding experience
7 years of employment as a software developer
Molecular Biology, Molecular Biology at Harriet L. Wilkes Honors CollegeNanodegree, Machine Learning Engineer, Nanodegree, Machine Learning Engineer at UdacityMaster's degree, Data Science, Master's degree, Data Science at New College of Florida
English, Spanish, Italian
Stackoverflow
18,298reputation
5.5mreached
100answers
29questions
jupyter-notebook
top-5%
git
top-1%
iterator
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pickle
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python
top-5%
pytorch
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Github Skills (20)
git10
python10
ray10
documentation10
data-science9
pickle9
xgboost9
pytorch9
jupyter-notebook9
iterator9
optimization6
deep-learning6
scikit-learn6
machine-learning6
ipython6
Programming languages (20)
Github contributions (5)
ray-project/ray
Nov 2022 - Nov 2022
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Back-end Developer & Technical Writer
Back-end Developer & Technical WriterContributions:36 reviews, 1 commit, 80 PRs in 1 day
Contributions summary:Ricardo primarily contributed to the Ray project by fixing bugs related to the AIR (AI Runtime) and Data modules, specifically addressing issues with failure configurations and the `to_pandas()` function. They also added missing dependencies for XGBoost examples and improved documentation, including clarifications for the Pytorch ResNet batch prediction tutorial and the Ray Data Quickstart. Furthermore, they made improvements to various examples, fixing runnable-related errors and making output less verbose.
pythonconsistsruntimetensorflowserving
crypdick/precision-recall-gain
Mar 2021 - Jan 2025
Contributions:12 pushes, 1 branch, 1 tag in 3 years 10 months
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