My journey to Machine Learning Engineering

The Early Years

I dove into the basics of JavaScript and PHP around age 11. My parents had shelves full of web development books, and while my brother and I were restricted to one hour of online gaming a week, coding didn’t count as “gaming.” So, naturally, we spent our time building websites.

By 14, I had moved on to C and Linux. I installed Ubuntu on an old Lenovo netbook, mastered the shell, and even experimented with penetration testing using an external Wi-Fi antenna. It was a thrill to understand how systems worked under the hood.

Discovering Data Science

In high school, I completed a CISCO networking certification and learned Pascal, though I found myself looking for harder challenges. The turning point came at age 16 when I found a library book on Linear Programming. I was fascinated by the idea of mathematically finding “optimal solutions” rather than using brute force.

This led me to neural networks. The concept that a model could predict patterns too complex for the human mind to comprehend sparked a curiosity that eventually steered me toward Data Science.

Physics vs. Computer Science

Before university, I was heavily inspired by channels like Jake Wright and spent hours solving Project Euler problems. I was set on a CS path until my parents suggested I avoid the “mainstream” route. I pivoted to Physics at Charles University in Prague, reasoning that a physicist could always return to CS, but not vice versa.

While I loved the creative process of deriving and testing theories, I eventually realized academia wasn’t for me. The field was hyper-competitive, and I wanted more financial stability and tangible impact. Plus, I missed coding.

Fun Fact: My first end-to-end ML project during university was detecting a bus at the stop outside my dorm using a webcam. I built a model to predict how many minutes late it would be based on previous observations!

The Professional Pivot

I realized that web dev is not for me. I landed my first role as a Data Scientist at Home Credit International. I spent about 50% of my time developing in Python, but I didn’t stop there. I used this time to expand my technical horizons, learning Rust and Go, and I helped launch a Data Science section at Nova TV.

During this period, I was constantly coding in my spare time. I worked on many fun side projects, most of which remain private experiments—but they were crucial for sharpening my engineering skills and keeping my curiosity alive.

Where I Am Now

Today, I work at Cisco as a Machine Learning Engineer. It is, without a doubt, the best workplace I’ve ever been part of. It allows me to sit exactly where I want to be: writing production level code while keeping a strong hand in Machine Learning.

I built this site to share that journey. Here, you’ll find my personal blogs, thoughts on technology, and a showcase of the projects I’m working on.