Experience & Projects

  • Jul 2025 – Sep 2025

    Software Engineer — Techex

    Worked on performance optimisation and streaming infrastructure at Techex.

    • Refactored React components to avoid unmount/remount cycles, optimised MobX usage, and stabilised the UI
    • Implemented process-specific CPU usage tracking and real-time charts for module statistics
    • Developed WHIP/WebRTC Rust output module to parse incoming PES packets and stream RTP packets, debugging with Wireshark
    • Automated encoder video quality testing (VMAF), saving over a week of manual effort per test round
  • Feb 2025 – May 2025

    Full-stack Car Inventory & Invoicing System

    Built a complete vehicle inventory and invoicing system.

    • FastAPI backend with SQLite database
    • Custom JS/HTML/CSS frontend with responsive layout
    • Automated data ingestion using a Selenium scraper with cron scheduling
    • PDF invoice generation with digital signing for secure payment tracking
  • Aug 2024 – Ongoing

    Self-hosted Server

    maxkendall.com

    Self-hosting multiple services and applications on a Raspberry Pi Zero W.

    • Hosting portfolio, Minesweeper, and personal todo-list web apps
    • Configured Nginx reverse proxy, Cloudflare DDNS via API, and CI/CD with GitHub Actions
    • Implemented SSH access with keys + fail2ban for security
    • Automated off-site snapshot backups for resilience
    • Monitoring with Prometheus + Grafana for remote alerts
  • Nov 2024 – Dec 2024

    Convolutional Neural Network (from scratch in C++)

    github.com/madmax755/cnn-from-scratch

    Built a CNN library from scratch in C++ for image classification.

    • Implemented convolutional, pooling, and dense layers
    • Custom Tensor3D class for efficient data handling
    • Custom optimisers (SGD, Momentum, Adam, AdamW with gradient clipping)
    • Model serialisation (save/load)

    Results:

    • Achieved 99.4% accuracy on MNIST with augmentation (demo)
  • Sep 2024 – Oct 2024

    Feedforward Neural Network (from scratch in C++)

    github.com/madmax755/mlp-from-scratch

    Implemented a classic MLP neural network entirely in C++ without ML libraries.

    • Highly configurable network topologies with multiple activation functions (ReLU, Sigmoid, Softmax)
    • Optimisers: SGD, Momentum, NAG, Adam, AdamW
    • Multi-threaded training support
    • Model persistence and evaluation metrics

    Results:

    • Reached 91% accuracy on MNIST digit classification