Rowan Nepulangoda
Deep Learning Software Engineer @Utfr - University Of Toronto Formula Racing
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WORK HISTORY
Deep Learning Software Engineer @Utfr - University Of Toronto Formula Racing
Toronto, ON, CA
Developed computer vision models in PyTorch for autonomous vehicle perception using labeled samples.• Improved model inference speed by 30% via pruning and architecture design maintaining <5% accuracy loss.• Reduced false detection rate by 18% through hyperparameter tuning and data augmentation strategies.• Integrated models with ROS for real-time deployment under strict latency constraints (<50ms inference).
EDUCATION
Neil McNeil High School
High School Diploma
University of Toronto Engineering
Bachelor of Applied Science - BASc, Industrial Engineering
ABOUT ROWAN NEPULANGODA
I’m an Industrial Engineering student at the University of Toronto with a strong interest in building scalable software systems and data-driven solutions that create measurable impact. My work sits at the intersection of software engineering, data processing, and machine learning, with a focus on turning structured and unstructured data into reliable, high-performance systems.During my recent internship as a Full Stack Software Engineering Intern at INK Entertainment, I designed and deployed a full-stack employee management system used by 200+ internal users. I engineered backend services with Node.js and Microsoft SQL Server, handled daily queries, and reduced database query latency by 45% through indexing and query refactoring. I also integrated AWS S3 and Lambda to support automated workflows, strengthening my experience with cloud-based architectures and data pipelines.Beyond backend development, I’ve worked extensively with machine learning and data modeling. As a Deep Learning Software Engineer with the University of Toronto Formula Racing team, I developed computer vision models using PyTorch and Scikit-learn on datasets of labeled samples. I improved inference speed by 30% while maintaining model accuracy, and deployed models within strict real-time latency constraints. This experience deepened my understanding of optimization tradeoffs between performance, accuracy, and system integration.My academic and personal projects further reflect my analytical approach. I’ve processed and modeled large-scale datasets in Python and SQL, built forecasting tools, and formulated high-dimensional optimization models using AMPL and Gurobi to evaluate business constraints and tradeoffs. I enjoy designing structured solutions to complex problems, whether that’s optimizing database performance, improving model efficiency, or analyzing patterns in real-world data.I’m particularly interested in roles that allow me to work on backend systems, data pipelines, machine learning applications, or analytics platforms where technical decisions directly influence performance and user experience. I’m motivated by measurable improvement, clean system design, and collaborating with teams to build solutions that scale.Seeking a 2026 Summer Internship dealing with high-impact projects that combine Machine Learning Analytics with Software Design.
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