What does it take to build a career in AI when the technology changes faster than any university curriculum can keep up?
Joana Fonseca is an AI engineer at TRATON with a PhD in machine learning and robotics from KTH Royal Institute of Technology. Yet even after a bachelor’s, master’s, and PhD, she had never learned or used generative AI. When she finished her PhD, she had to learn transformers and generative AI herself. In this episode of Builders, Joana talks about the foundations AI engineers need, why continuous learning is unavoidable, and what separates someone who can build an impressive prototype from someone who can build reliable, valuable AI systems.
She also discusses the importance of domain collaboration and data engineering, what makes a company truly AI-native, where vision language models (VLMs) and vision language action models (VLAs) are heading, and what AI could look like by 2030.
Joana also shares what she is seeing through Stockholm AI and why the conversation around AI increasingly extends beyond models into infrastructure, governance, policy, and societal impact.
Chapters
00:00 VLMs and autonomous driving
00:34 Introduction
00:48 What does it take to become an AI engineer?
01:38 The foundations every AI engineer needs
02:03 Why continuous learning is unavoidable
02:37 Even PhDs had to learn generative AI
03:05 Learning AI after a PhD
04:39 How important is domain expertise?
05:04 Why collaboration matters in AI
06:15 What separates strong AI engineers?
06:31 Building AI beyond the prototype
07:04 The importance of data engineering
07:17 What the AI community is talking about
09:30 AI's impact beyond technology
10:50 What makes a company AI-native?
12:30 What's next for VLMs and VLAs?
13:30 The causality problem in autonomous driving
16:00 How researchers are improving VLAs
17:00 What will AI look like in 2030?
19:00 Advice for the next generation of AI engineers
19:22 Why lifelong learning matters
