Welcome to MVST, Llorenç

We’re excited to welcome Llorenç Sastre to the MVST team! 🎉 Joining us as a Machine Learning Engineer, Llorenç brings a strong background in telecommunication engineering and a clear passion for turning machine learning ideas into practical, real-world solutions. Always curious about how technology becomes usable products, he thrives at the intersection of models, systems, and impact. When he’s not exploring data and algorithms, you’ll most likely find him underwater 🌊

About Llorenç

• Full Name: Llorenç Sastre
• Home Country: Spain 🇪🇸
• Position: Machine Learning Engineer

A small introduction about you ✨

Hi! My name is Llorenç Sastre, and I’m a Junior Machine Learning Engineer with a background in telecommunication engineering. I’m especially interested in applying Machine Learning to solve real-world problems and understanding how ideas turn into usable products. Outside the work, I’m very connected to the sea, I enjoy scuba diving, apnea, and all sorts of water sports. Being underwater helps me disconnect, stay curious, and keep clear mindset, which I think also translates to how I approach problem-solving.

If you could instantly become an expert in something, what would it be and why?

I'd choose decision-making under uncertainty. In machine learning and AI, a lot of real value comes from making good decisions with incomplete or noisy information, whether it’s in models, systems or product choices. Being an expert in this would help me design solutions that are not only technically correct, but also robust and practical in real, imperfect enviroments.

If you could invent a product or service that would make people's lives better, what would it be?

I'd like to build AI tools that work quietly in the background to reduce cognitive overload at work. Not tools that constantly demand attention, but systems that organize information, anticipate needs, and simplify complex workflows. The goal would be to help people focus better, make clearer decisions, and reduce unnecessary mental stress.

Do you have any helpful advice for people that want to pursue the same career as you?

Don’t rush to specialize too early. Take time to build a solid understanding of fundamental, not only in machine learning, but also in software and systems. Try things, make mistakes, and work on real projects as soon as possible. And most importantly, stay curious: the people who grow the most in this field are the ones who genuinely enjoy learning.

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