Santiago Viquez
Developer Relations
Costa Rica
Summary
🤩
Rockstar🎓
Top SchoolSantiago Viquez is a Developer Relations professional and ML-savvy generalist with nine years of experience helping teams design, ship, and adopt data-driven solutions across startups and enterprises. Based in Costa Rica, he blends hands-on machine learning engineering—contributing typed pipelines and scheduler improvements to the popular Hugging Face diffusers project—with developer advocacy roles at NannyML and Soda. His background in physics and a master's in data science underpin pragmatic work in supply-chain optimization and recommendation systems that delivered multimillion-dollar savings and production-ready analytics at companies like UPS and Walmart. A former data analytics instructor, he’s comfortable translating complex models into teachable workflows and reproducible code, and he often focuses on maintainability and type-safety in ML codebases—an approach that quietly improves long-term collaboration.
9 years of coding experience
8 years of employment as a software developer
Master's degree Data Science, Master's degree Data Science at Università degli Studi di PadovaBachelor's degree Physics, Bachelor's degree Physics at Universidad de Costa Rica UCR
English, German, Spanish
Github Skills (10)
diffusion-models10
diffusion-probabilistic10
machine-learning10
pytorch10
deep-learning10
python10
diffusers10
diffusion-probabilistic-models10
flax4
jax4
Programming languages (6)
Github contributions (5)
huggingface/diffusers
Sep 2022 - Sep 2022
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 5 PRs, 3 comments in 4 days
Contributions summary:Santiago primarily contributed to type hinting various pipelines and scheduler implementations within the diffusers library. They focused on the score SDE VE, latent diffusion uncond, and several scheduling algorithms, including DDIM, Karras VE, and LMS discrete. Their contributions improved code clarity and maintainability by adding type annotations, aligning with best practices for this deep-learning project. Several commits also included code review suggestions from other contributors.
pytorchartdeep-learningimage2imagestate-of-the-art
NannyML/The-Little-Book-of-ML-Metrics
Oct 2024 - Mar 2025
The book every data scientist needs on their desk.
Contributions:27 reviews, 38 PRs, 72 pushes in 4 months
classification-metricsclustering-metricscomputer-vision-metricsdata-sciencemachine-learning
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