# Santiago Giron

> Machine Learning Engineer @ Zendar | Deep Learning, Computer Vision, Autonomous Systems

Location: San Francisco, California, United States
Profile: https://flows.cv/santiagogiron

Machine Learning Engineer with expertise in computer vision and deep learning, specializing in 3D perception systems and point cloud processing. Proven track record developing production ML pipelines for spatial data analysis and semantic segmentation. Experience deploying models at scale using cloud infrastructure and implementing real-time processing systems. Strong background in LiDAR point cloud processing, sensor fusion, and distributed computing.

## Work Experience
### Machine Learning Engineer @ Zendar
Jan 2025 – Present | Berkeley, California, United States

### Perception Software Engineer @ Zendar
Jan 2024 – Present | Berkeley, California, United States

### Data Scientist @ Density
Jan 2023 – Jan 2024 | San Francisco, California, United States
I engineered an advanced algorithm to generate noise filters for radar-based point cloud data incorporating XGBoost for radar data classification. I designed and implemented evaluation metrics for assessing point cloud clustering and object tracking performance from millimeter wave radar data using Python and NumPy. I contributed to the creation of a comprehensive computer vision-based object tracking dataset to establish ground truth for validating millimeter wave tracking accuracy.

### Software Engineer @ Density
Jan 2022 – Jan 2023 | San Francisco Bay Area
I designed and deployed a high-precision point cloud processing pipeline for the production of sub-centimeter resolution, large-scale indoor digital elevation models. I developed a novel ceiling height detection algorithm leveraging deep learning-based semantic segmentation to handle complex multi-level environments with obstacles. I also extended the cloud API infrastructure to enable efficient storage and retrieval of large-scale raster datasets.

### Imaging Software Engineer @ Gamma Reality Inc.
Jan 2021 – Jan 2022 | Richmond, CA, United States
I integrated new GPS, IMU and LiDAR sensors with GRI's Localization and Mapping Platform (LAMP) while expanding the image reconstruction framework through GPS and AHRS based localization using an extended Kalman filter and the Robot Operating System (ROS) framework. Additionally, I led the optimization of core Docker infrastructure by implementing multi-stage builds and dependency management, which successfully reduced image size and build times.

### Spatial Deep Learning Intern @ HELIX RE
Jan 2020 – Jan 2021 | Berkeley, California, United States
I developed and deployed a deep convolutional neural network, using the ResUNet architecture with sparse 3D convolutions, for semantic segmentation of building elements in large-scale LiDAR point clouds. During training, I engineered an ML data pipeline implementing multiple augmentation techniques including denoising, anisotropic scaling, and elastic distortion to improve model generalization. The system was deployed using PyTorch models in CUDA-enabled Docker containers on GKE clusters, with Pub/Sub handling asynchronous inference requests in production.

### Software Development Intern @ Health2047 Inc.
Jan 2016 – Jan 2016 | San Francisco Bay Area
Using data from the U.S. Department of Veterans Affairs Open Data Portal API, I developed an interactive geographic visualization of VA healthcare coverage across the United States. I aggregated county-level enrollment data and used Python's Bokeh library to create an interactive map displaying both VA facilities and healthcare enrollee distributions. Additionally, I developed frontend components for a comprehensive EHR system, building cross-platform mobile interfaces with Xamarin/.NET and implementing key web portal features using React.


## Education
### Bachelor’s Degree in Computer Science
University of Chicago


## Contact & Social
- LinkedIn: https://linkedin.com/in/santiago-giron-16483285

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Source: https://flows.cv/santiagogiron
JSON Resume: https://flows.cv/santiagogiron/resume.json
Last updated: 2026-04-05