Motorola Solutions - Machine Learning Engineer
May 2021 - January 2022 | Somerville, MA
The Social Distancing Detective
Remember when social distancing was the hot topic? I was building AI systems that could automatically track and monitor social distancing compliance using deep learning! It was like creating a digital guardian angel that could watch over public spaces and help keep people safe during the pandemic.
The Technical Arsenal
Working with PyTorch, TensorFlow, OpenCV, and Scikit-learn in Python, I built computer vision systems that could analyze video feeds in real-time. The coolest part? Using ONNX to optimize models for different deployment environments and FastAPI to create lightning-fast server endpoints.
Cloud-Scale Video Analysis
Managing video data through AWS S3 and MongoDB was like orchestrating a digital film festival where every frame mattered. The system could process thousands of hours of video footage, extracting meaningful insights about human behavior and safety compliance.
The Container Revolution
This was where I really fell in love with Docker and Kubernetes! Containerizing AI models and orchestrating their deployment felt like building digital LEGOs - each piece perfectly designed to work together. The result? Seamless deployment and bulletproof dependency management that made system administrators very happy.
The Speed Demon
One of my proudest achievements was using quantization, pruning, and network surgery techniques to reduce model inference latency by 30%. It’s like giving your AI system a turbo boost - same intelligence, much faster execution!
Face Recognition Adventures
Building a proof-of-concept for face recognition was like teaching machines to recognize friends at a party. Using PyTorch and distributed computing techniques, I reduced false negatives by 30% while accelerating training by 40% using GPU parallelization (DDP, Sharding).
The DevOps Journey
Working with Bamboo for CI/CD taught me the importance of monitoring important metrics and maintaining smooth deployment pipelines. It’s like having a health monitoring system for your AI - always checking that everything runs smoothly in production.
Cross-Functional Collaboration
Working with hardware engineers, product managers, and field deployment teams was fascinating. I learned how AI solutions need to work in real-world environments - from harsh weather conditions to varying lighting scenarios.