Alan Descoins
Chief Executive Officer at Thaloz
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlan Descoins is a seasoned software leader and CEO with 14 years of experience building AI-driven products and scaling engineering teams from early-stage to executive leadership. Based in San Francisco, he rose through Tryolabs from one of its first engineers to CTO and now CEO, with deep hands-on expertise in Python, Django, cloud infrastructure, and production NLP/AI systems. He combines product-first leadership with practical ML engineering skills, contributing to notable open-source computer vision projects like Norfair and Luminoth where he improved demos, compatibility, and packaging for real-time tracking and vision toolkits. His background includes enterprise-grade development at IBM and a track record of shipping secure, scalable services across healthcare, real-time analytics, and content platforms. Known for pragmatic engineering processes, he also advises remote talent platforms and invests in developer-first companies. He brings a rare blend of operational leadership and low-level ML implementation experience, often stepping into code to solve production bottlenecks.
14 years of coding experience
13 years of employment as a software developer
Engineer’s Degree Computer Engineering, Engineer’s Degree Computer Engineering at Universidad de la República
English, Spanish, Portuguese
Github Skills (15)
multi-object-tracking10
object-detection10
computer-vision10
faster-rcnn10
machine-learning10
deep-learning10
tensorflow10
py10
python10
setuptools10
yolov79
detectron9
yolov49
pytorch9
testing7
Programming languages (12)
Github contributions (5)
tryolabs/norfair
Sep 2020 - Oct 2022
Lightweight Python library for adding real-time multi-object tracking to any detector.
Role in this project:
ML Engineer
ML EngineerContributions:6 releases, 45 reviews, 122 commits in 2 years 1 month
Contributions summary:Alan primarily contributed to the project by updating and refining the demonstration code for various object tracking and detection models, including Detectron2, YOLOv4, YOLOv7, AlphaPose, and integrating with OpenPose. They modified the demo scripts to ensure compatibility, convert data formats, and fix paths. Additionally, they incorporated code styling and testing practices, enhancing the project's maintainability.
multi-object-trackingpythontrackingtracking-algorithmkalman-filter
tryolabs/luminoth
Aug 2017 - Jan 2020
Deep Learning toolkit for Computer Vision.
Role in this project:ML Engineer
ML EngineerContributions:2 releases, 134 commits, 24 PRs in 2 years 4 months
Contributions summary:Alan contributed significantly to the Luminoth project, focusing on the model's setup, versioning, and structure. Their commits include adding classifiers and meta information to the setup.py file, updating versions, and improving the setup process. Additionally, they addressed minor issues like typos in comments, contributing to overall code quality and maintainability within the deep learning toolkit.
computer-visiondeep-learningtensorflowsonnetobject-detection
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