Out of the Research Lab and Into Production: My Path to Leverege

After years of deployment cycles measured in months, I found a place where what I build in the morning can reach a customer the same afternoon.

Alessandro Cennamo
Senior Machine Learning Engineer

I started my career about eight years ago, after completing my master's degree in Italy and moving to Germany. My first role involved an industrial PhD program at a large automotive company, focusing on radar-based AI for autonomous driving. I published several papers, filed patents, and eventually transitioned into a full-time engineering position. At my second job, I started as a junior engineer and was quickly promoted to lead a team of five or six people, gaining my first significant experience in leadership and team responsibility.

But after a while I started to feel like I wanted something different. Both companies I had worked for were large, north of 150,000 employees. Things move at a certain speed in organizations like that, and there are a lot of layers between your work and the customer. I wanted to build things people use directly, not components that just disappear into the code. That's what I was looking for when I started looking around.

A Thursday Message, a Friday Call

I came across Leverege through a LinkedIn post from Hannah, our CPO. I reached out directly and messaged her on a Thursday. By Friday we were already on a call. At the time, I had another offer on the table, but I chose Leverege because of something I felt from the very first conversation: that everyone here had a sense of purpose and an understanding of what the objective was. In bigger companies that clarity can get lost as things scale up. At Leverege it’s always stayed crystal clear.

Shipping to Production Within Weeks

Within just a few weeks I already felt like I was contributing. The feedback loop here is short in a way that you might not appreciate unless you've experienced the opposite. Here, what I work on in the morning can affect what the customer sees within a few hours.

My first independent project was a video security feature. A few weeks later, we had a trained model and an initial implementation running on the customer's system. Coming from environments where deployment cycles were measured in months, that was an incredible shift.

The Day-to-Day as an ML Engineer

I work on PitCrew, our product for automotive service centers. I build and maintain the algorithms that generate analytics for customers: service counts, customer foot traffic patterns, that kind of thing. A significant portion of my day is making sure what's in production is working as expected, and collaborating with the product team when issues surface.

I'm based in Europe so I'm a few hours ahead of my US colleagues. Sometimes I wake up and see an issue reported the day before, and it's fulfilling to end my day with that issue resolved and the change pushed into production. 

The long-term work runs in parallel. I make sure we have the data we need, that models are properly trained and annotated, and that what's in production stays sharp. Sometimes there is the opportunity to build a feature from the ground up. For example, once a customer asked for a system to detect when people were entering or leaving their store. A couple of weeks later we had a working solution deployed. For an ML engineer who wants to see their work actually land, that rhythm is hard to beat.

What I've Noticed About the Culture

The thing I've noticed most at Leverege, compared to the larger organizations I came from, is how people approach helping each other. People are genuinely eager to help and it happens proactively. You don’t even necessarily need to ask for help to get it — sometimes people seek you out to help you or just check in. That's been consistently true since I joined.

Working fully remote for the first time has also been an adjustment, but a good one. The flexibility is real. The tradeoff is that you have to be self-directed: it's on you to make sure you're working on the right problem at the right time with the right priorities. Leverege prioritizes providing the agency to do that without micromanagement, which I think is the right environment for an engineer who wants to own their work.

If you're an ML engineer thinking about making a similar move: the qualities that matter most here are being open to helping others and being driven by the work. You need that passion that pushes you to keep improving yourself, the product, and the people around you.

~ Quick Facts ~

  • My Leverege Shoutout: Pedro manages to do a lot of things without compromising on quality, and he's always willing to help even when he's stretched thin.
  • Life Outside of Work: I just moved back to Italy so right now I'm still finishing unpacking. Once summer comes I'm looking forward to taking my motorboat out with friends, cooking seafood on board with camping equipment. Clams, shrimp, tuna, some champagne. I consider myself an expert in this.
  • Content Recommendation: The Intelligent Investor by Benjamin Graham. I got into economics and investing during COVID and this book changed the way I think about it. Technical in places but worth it if you're interested in investing.
  • Could Give a TED Talk About: Planning a proper boat trip. My friend and I are pretty meticulous about it — the provisions, the seafood, the setup for cooking on board. It sounds simple but there's real craft to it.

Alessandro Cennamo

Senior Machine Learning Engineer

With over 7 years of experience in the automotive industry, Alessandro has led cross-functional teams and driven the development of computer vision and ML-based perception solutions using radar, LiDAR, and camera data, spanning the full ML lifecycle - from data collection/annotation, through experimentation/evaluation to deployment. He loves using technology to design and develop solutions that solves customer pain-points. Outside work, you could not find Alessandro because he is probably out at sea fishing on his boat with friends. Other activities Alessandro enjoys are watching soccer games, tasting a good wine and spending time with his family.

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