2026
Machine Learning Research
Conducting in-depth research into machine learning architectures and deep learning. Currently focused on self-attention mechanisms and Transformer architectures. I've developed three projects: a simple perceptron, a Transformer model trained on motivational phrases, and another trained on Shakespeare texts. Building from the ground up to deeply understand how neural networks operate at their core.
2025
Systems Analysis & Development - IFSC
Pursuing a technologist degree at IFSC (Federal Institute of Santa Catarina) in São José, specializing in software development. The program provides comprehensive education in systems design, software architecture, and development practices. Each semester brings new technical challenges and strengthens theoretical foundations in computer science.
2025
2024
Young Apprentice Program - Intelbraz
Worked in an agile team of 8-10 people on embedded systems for network routers based on OpenWrt. Responsible for implementing and validating new firmware features, conducting system testing, and debugging embedded Linux systems. Gained hands-on experience with firmware validation workflows and collaborative agile development in a professional environment.
2025
2024
Back-end Development Course - SENAI
Completed a comprehensive back-end development bootcamp at SENAI, my first formal introduction to professional programming. Gained expertise in full-stack development: front-end (HTML, CSS, JavaScript), back-end systems, API development, and relational database design. This program laid the foundation for my career in software development.

About Me

Hi, I'm Marcos Júnior, a machine learning researcher and developer passionate about exploring the boundaries of artificial intelligence. I specialize in building and training deep learning models using PyTorch, with a focus on Natural Language Processing (NLP) and Multi-Layer Perceptron (MLP) architectures.

Currently, I'm consolidating my machine learning foundations while diving deep into Transformer architectures and self-attention mechanisms—the backbone of modern NLP. I'm actively researching how these cutting-edge techniques work from first principles, with the goal of building a significant project that demonstrates mastery of these architectures. My learning journey combines theoretical knowledge with hands-on implementation of complex models.

Beyond ML research, I have a strong foundation in full-stack software development and embedded systems. This diverse technical background allows me to approach AI problems with both a research mindset and practical engineering sensibilities. I'm committed to continuous learning and contributing meaningfully to the AI community.