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AI, ML, frontend, backend. Leverege is where it all fell into place.
There are a lot of things I could say about why Leverege is a good place to work. The projects are genuinely interesting. The remote culture is well-run. The people are great to work with. But if I had to name the one thing that really sets the place apart, it is this: the company is built for people who want to keep learning, and it actually means it.
I have always been driven by learning. That is one of the things that attracted me to mathematics in the first place, and it later drew me to extend from a mathematics professorship to statistics, and from statistics to machine learning and AI. When I decided to move from academia to the business world, I moved to a field that rewards exactly that kind of drive.
AI is not a field where you can settle in and simply repeat what you already know. It is moving too fast for that. That is true whether you are on the technical side or in a less technical role, and at Leverege, there is no real non-technical role because everyone is engaged with the technology at some level.
When I was looking for a role in industry, I was not just looking for interesting technical problems. I was also looking for a company that understood how fast-moving the field of AI is and that was oriented accordingly. A company that stays on top of new tools, encourages people to dive in and figure out how to use them even when there is not yet much guidance or consensus, and treats the constant state of flux as an opportunity rather than a disruption. That is what I found at Leverege.
Right now I am building systems that use computer vision to help improve workflows in wind blade manufacturing. The work involves gathering information from cameras, turning it into useful data, and applying it to improve the manufacturing process and automate the more tedious and labor-intensive parts of it. One interesting part of the project is that it has not been done before. There is no template. That is true of a lot of what happens here: a customer brings a pain point, and we figure out how to solve it, which requires people from product, design, software development, and machine learning to work through the problem together. On the ML side, that can span camera setup, data collection, finding a suitable model, training the model, and figuring out how to make model outputs actually useful for the problem at hand. Depending on your background, you might focus tightly on the modeling work or extend into MLOps and deployment. The scope adjusts to what you bring.
Communication in a university setting and communication in a company are different, and transitioning from one to the other is kind of like learning a new language. You know what you want to say, but you have to learn how to say it in the new context.
When I made the transition from academia to business, it helped that a lot that the people at Leverege recognized that I was coming from a different environment, was eager to learn, and had perspectives worth having even if I was newer to the business world. I was supported through that adjustment, and I am grateful for it. I think it reflects something real about the culture: the company values your experience and your potential, not just where you already are.
Really thriving here comes down to two things. The first is a team orientation. The culture at Leverege is built around supporting each other, and if your instinct runs toward competing against your colleagues, that will not fit. The second is genuine curiosity. Not just tolerance for change, but an actual appetite for it. I have talked with people elsewhere who find the pace of change in AI exhausting and would rather just use what they have already learned. I can understand that, but this is not the right environment for that.
If, on the other hand, you are someone who gets energized by a field that never quite settles down, who is drawn to problems that have not been solved before, and who wants to be at a company that stays on top of new tools and actively figures out how to put them to use, this is a genuinely good place for that.
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