Machine Learning Engineer - Computer Vision & AI Systems Specialist

Jerry (Yunjie) Zhang

Atlanta, GA · 678-***-**** · **********@******.*** · github.com/Jer-Bear124

Education

Georgia Institute of Technology Fall 2026

Bachelor of Science in Computer Science, Zell Scholarship Atlanta, Georgia Relevant Courses: Design/Analysis of Algorithms, Data Structures, Computer Systems and Networking, OOP, Probability and Statistics, Perception and Robotics, Machine Learning, Operating Systems, Computer Vision Technical Skills

Programming Languages: Python, Java, C/C++, SQL, JavaScript, Assembly, R, HTML/CSS, Bash, Slurm, XML/YAML

Libraries & Frameworks: PyTorch, NumPy, Pandas, OpenCV, TensorFlow, Scikit-learn, Flask, React, Django, Streamlit Tools & Environments: Linux (RHEL-8,9, Ubuntu), Git, GitHub, GitLab CI/CD, Docker, IntelliJ, PyCharm, VS Code, Visual Studio, Anaconda, Arduino

Experience

Machine Learning Engineer Intern May 2025 – Present Electro-Optical Systems Laboratory, GTRI Atlanta, GA

• Enabled large-scale ML simulation by streamlining Unreal Engine deployment (saving 500+ hours), architecting a synthetic data pipeline, and resolving a critical data-loss issue in the Microsoft AirSim API.

• Developed a real-time image stitching pipeline using OpenCV (SIFT/AKAZE) to process multiple UAV camera feeds at 15 FPS, creating panoramic views for a mission-critical defense application.

• Engineered CI/CD pipelines and data visualizations for the open-source LLM-Guided Evolution project, a framework cited in Google DeepMind’s AlphaEvolve whitepaper. Undergraduate Research Assistant January 2024 – May 2025 Automated Algorithm Design Research Group Atlanta, GA

• Led a 15-person team in applying Neural Architecture Search & LLM-driven techniques (Evolution of Thought) to evolve YOLO object detection models for self-driving, achieving a 60% improvement in Mean Average Precision.

• Slashed compute time by 70% by architecting a streamlined workflow on an HPC cluster, managing a SQL server and optimizing resource allocation with SLURM.

• Orchestrated an LLM-driven evolution process to generate high-performing object detection models from seed architectures (YOLOv3, ResNet, FasterRCNN, etc.), designing end-to-end pipelines for multi-GPU scalability. Projects

xv6 OS Virtual Memory Optimization C, x86 Assembly, GDB October 2025

• Implemented Copy-on-Write (COW) forking and lazy zero-page allocation in the xv6 kernel to reduce memory usage and latency by 85% for the fork and sbrk system calls.

• Engineered a physical page reference counting system and modified the page fault interrupt handler to facilitate on-demand duplication of shared memory pages upon a write attempt.

• Collaborated on the system architecture and authored user-space test cases to validate kernel correctness and ensure full binary compatibility.

Medical AI Assistant (What’s Up Doc) - Hackathon(Hacklytics) React, Flask, Gemini API February 2025

• Developed a full-stack AI medical assistant using Flask and React, leveraging Google’s Gemini API with a Retrieval Augmented Generation (RAG) pipeline to provide drug information.

• Engineered key features including patient record analysis via file upload and sentiment analysis of simulated patient communications to enhance diagnostic context. Autonomous Robot Platform Project C++ July 2024

• Engineered an autonomous stair-climbing robot from concept to prototype within a Georgia Tech entrepreneurial program focused on developing products for edge devices and IoT.

• Programmed precise sensor and motor control systems in C++ on an Arduino platform, achieving a 90% success rate in repeated stair-climbing trials.

• Awarded Best Project among 20 competing teams in recognition of superior design efficiency and functionality. Spotify Wrapped Website Project Django, Python, AWS October 2024

• Developed a full-stack Django application that integrates the Spotify API, allowing users to retrieve, analyze, and compare personal listening histories with friends.

• Designed optimized database models and deployed the application to a scalable AWS environment using EC2, RDS, and S3 with CloudFront.

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