Career growth at ML6 isn't a fixed ladder. Some engineers move into Tech Lead and Squad Lead roles. Others specialise more by building deep technical and domain expertise. What's consistent across every story: leadership arrives early, hard problems are treated as the interesting part of the job, and culture keeps coming up as a reason people stick around.
If you're a senior engineer, you've probably heard every version of the “come build the future with us” pitch. What's harder to find is a straight answer to the question that actually matters: what does real career growth look like at an engineering company?
We asked our own engineers. Not a marketing copy, just their honest answers about how they got here, what challenged them, and what they'd tell another engineer weighing their next move.
What stands out first is how differently everyone's ML6 story starts. In this blogpost Thomas Uyttenhove (Senior ML Engineer, Squad Lead), Roelof Kuipers (Senior ML Engineer), Yanou Ramon (AI Engineer) and Enes Yesilyurt (Senior Software Engineer) will testify on their journey at ML6.
Thomas joined straight out of Ghent University in 2020 and started as a Machine Learning Engineer. Enes joined the Berlin office in 2025 as a Senior Software Engineer, after a career that drifted from mechanical engineering into software and then deeper into AI.
Roelof discovered ML6 through a blogpost on dynamic pricing. He joined as a Senior Machine Learning Engineer focused on the energy sector, pulled in by the mix of state-of-the-art AI work, dedicated research time, and company culture. Yanou joined the Amsterdam office recently after being employed at an E-commerce scale up where she focused on AI research, digital strategy consulting, and AI engineering. She was looking for a role that would let her build AI applications end to end, not just the models, but the infrastructure to actually ship them at enterprise scale.
Different backgrounds, different countries, different entry points. What they all share is a deep passion for technology and the desire to see real AI applications in production.
Ask four engineers what their growth trajectory looked like, and you'll get three different answers. Nobody's growth was handed to them on a predefined track. It was shaped by the problems they chose to lean into.
For Yanou, a key highlight at ML6 is the flexibility to evolve your career path over time. Beyond technical work, she would love to learn more about people management and strategy: “I’m also involved in the Women in AI space, teaching, and managing partnerships with Microsoft and Anthropic. These activities have helped broaden my impact.”
Thomas: “My engineering role allowed me to pursue technical and functional interests and goals by delivering large scale projects, representing ML6 in pre-sales and conferences and finally shaping enterprise AI strategies.” It led him to a Tech Lead role owning end-to-end project design, and eventually to becoming a Squad Lead, now guiding a cross-functional team across Ghent and Berlin.
For Enes, growth arrived fast and came with weight attached. Within his first few weeks, he moved into a Technical Lead role on one of the company's largest finance-sector projects, leading the AI team on an engagement to help a major financial institution adopt AI responsibly and reproducibly.
For Roelof, growth was less about title changes and more about skill development. “What I like about being a Senior ML Engineer at ML6 is that it goes far beyond building models.” He specifically wanted to strengthen his cloud infrastructure and software engineering skills, and described the requirements of his role as a genuine mix: technical ML expertise, software engineering, cloud infrastructure, team leadership, and client management.
None of our engineers described their biggest challenge as “the tech was hard.” The hard parts were almost always about ambiguity, trust, and scale.
Enes talks about the pressure of stepping into a leadership role on a major finance project within weeks of joining, not just delivering a solution that exceeded expectations, but building a team environment where people felt trusted with real ownership rather than managed. “What made that possible was the team and colleagues around me. From day one, colleagues were generous with their knowledge and never hesitated to challenge my thinking.”
Roelof's challenge is structural to the kind of work ML6 takes on: co-developing highly technical solutions alongside stakeholders who are genuine domain experts. In energy projects like battery optimization for trading, the question is rarely just “can we build this?” It's how to translate a complex commercial, physical, and operational problem into something that's both technically sound and practically useful, often figuring that out together with the people who actually run the business. This mirrors a broader pattern: technical missions and trust in leadership matter more to senior engineers than perks alone.
While Thomas and Yanou mention the challenge of productionising enterprise scale solutions while balancing between technical and functional responsibilities. “Effectively leveraging the collective expertise within the company is therefore key”, highlights Yanou.
Ask about the coolest project, and the excitement is hard to miss.
Enes jokes: “My current project is the best project I've worked on at ML6. While that's easy to say since it's the only project I've worked on here so far, I genuinely mean it”. He's helping a major financial institution adopt AI responsibly, while leading a team of engineers and AI specialists and shaping both the technical direction and the team's growth.
Roelof points to an optimization platform he's building for a company delivering green power systems to large energy consumers, a project that combines optimization, forecasting, software engineering, and close collaboration with renewable energy experts, where he leads as Tech Lead shaping the technical direction.
For Yanou, one of the coolest projects she’s worked on has been with a government institution, where she built a Retrieval-Augmented Generation (RAG) solution that allows employees to safely query internal knowledge bases. “Deploying AI in a highly regulated environment while building an application for more than 20,000 users has made it both technically challenging and rewarding.”
Two pieces of advice came up, and they complement each other well.
Enes: “Becoming genuinely great at something creates opportunities you can't predict.” His suggestion is to stop optimizing for the “perfect” career path and instead focus on craftsmanship and consistently strong work, and to surround yourself with people you admire and want to grow alongside.
Roelof: “Focus on what you genuinely enjoy and where you can build real depth”. Particularly relevant in the GenAI era: in a world where AI can produce a plausible answer to almost anything, what actually differentiates you is personality, real experience, and clarity about what you're genuinely good at. He argues it's more powerful to be honest about your strengths and your limits than to try to be good at everything.
Yanou concludes: “Don’t underestimate the importance of the domain you’re applying the technology to. The most rewarding projects are those where you’re not only building something technically impressive, but solving a problem that truly matters.”
At ML6, ownership arrives from day one, not after years of waiting. Enes was leading a major finance project within weeks. Thomas moved from an ML Engineer role to Tech Lead to Squad Lead by following the problems he wanted to solve, not a fixed timeline.
It’s not per se about the technology itself, but about the problem you are solving. And there is more to it than bringing a solution to production. Ambiguous stakeholder requirements, regulated industries, physical and commercial constraints stacked on top of technical ones: these weren't complaints in our conversations, they were the parts people were most energized to talk about.
Our people and growth focused culture is a stated reason people stay, not just a line in a careers page. It came up unprompted in almost every answer on why people love working at ML6, but also stick around.
If you're an engineer trying to figure out whether your next move should be about a new title, a harder problem, or a better team, the honest answer from the people already living it is: at ML6, you don't have to choose just one. That's what career growth really looks like inside ML6 engineering.