Charlie Budd
Senior AI Systems Engineer - Surgical Robotics at Machnet Medical Robotics
London, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolCharlie Budd is a Senior AI Systems Engineer specializing in surgical robotics with nine years of combined industry and postdoctoral experience in computer vision, deep learning, and real-time physics simulation. He has designed and deployed CUDA-accelerated vision algorithms and RL-based tools that have been used in live human surgeries and clinical neurosurgical trials. Comfortable across Python, C++, PyTorch and low-level GPU work, he bridges research and product delivery—leading cross-disciplinary teams to ship GPU-streaming pipelines, simulation engines, and clinical-grade ML systems. An active contributor to MONAI, he brings practical medical-imaging optimizations (e.g., filtering and CRF work) into production toolkits. Self-taught as a software developer, he blends academic publication rigor with hands-on engineering that turns prototypes into commercial and surgical deployments.
9 years of coding experience
Master of Science - MSci Physics, Master of Science - MSci Physics at University of Birmingham
Github Skills (11)
medical-imaging10
pytorch10
deeplearning-ai10
deep-learning10
python10
imaging10
medical-image-computing10
medical-image-processing10
cprogramming-language9
c-language9
cuda9
Programming languages (6)
Github contributions (5)
Project-MONAI/MONAI
Dec 2020 - Jun 2021
AI Toolkit for Healthcare Imaging
Role in this project:
ML Engineer
ML EngineerContributions:15 reviews, 18 commits, 17 PRs in 6 months
Contributions summary:Charlie primarily contributed to the development and enhancement of machine learning components within the MONAI toolkit, focusing on medical imaging. Their work involved optimizing existing code, implementing profiling tools for performance analysis, and integrating new filtering techniques like the Permutohedral Lattice Filter (PHL). Further contributions included implementing and testing a Conditional Random Field (CRF) and addressing backward pass issues in a Bilateral Filter implementation.
pytorchpythondeep-learningmedical-image-processingcomputer-vision
charliebudd/torch-content-area
Dec 2021 - Jan 2023
A PyTorch tool kit for estimating the circular content area in endoscopic footage.
Contributions:6 releases, 1 review, 547 commits in 1 year 1 month
pytorchfootageendoscopytool-kitdeep-learning
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