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How Curalate Built a Kick-Ass Research & Development Team

The words “research and development” conjure images of people working diligently in underground, fluorescent lit rooms with checklists, clipboards, and large circuit breakers. Though possibly due entirely to B.D. Wong, this public perception of “R&D” is continuously fueled by the way leading companies portray their innovation teams. With names like Skunk Works to Google X, the message is clear: this work is need-to-know, and you don’t have the need.

This begs the question: does isolation breed innovation?

At Curalate, we believe it doesn’t. In fact, isolating R&D teams can be a risky, wasteful, and expensive practice that many large software companies seem all too happy to execute.

research & development isolation

The isolationist structure is often defined by roles. Engineers build software. Researchers solve academic problems. While attractive from a labor point of view, the resulting process of taking research and making it into a product (i.e., tech transfer) becomes incredibly risky and expensive. The reason is simple: an approach that works well in the lab may fail entirely when brought to life.

How can this happen? Often, it is a disconnect between the researchers and engineers. Researchers don’t get access to real world systems, resulting in algorithms that don’t scale. When such algorithms undergo tech transfer, engineers must degrade the core functionality to cope with this lack of robustness. The resulting lackluster product has the potential to impress but falls short because the input must be “just right”.

Such shortcomings are a direct result of isolation: segregating researchers and engineers prevent each from understanding the other’s work. This is the organizational equivalent of a middle school dance: researchers and engineers standing across the gym from each other, both longing for a deeper understanding of each others’ mythos.

research & development quote

At Curalate, we take a different approach.

We integrate these roles to form teams composed of research engineers. These individuals are devoted to both research and development. The resulting duality is extremely beneficial: scaling state of the art algorithms into products illuminates new research problems, which in turn can be built into new products. This productization-research loop occurs entirely within our R&D team, allowing Curalate to bring unique software to market faster.

This type of tight integration comes with it’s own set of unique challenges. Here are a few of the core philosophies that have enabled Curalate to build such a kick-ass R&D team.

research & development attitudes

Now, let’s break that down.

1. Keep your ear to the ground across fields. Staying up-to-date in both research and engineering will enable you to apply new information across functions.

2. Admit what you don’t know. A lot of people start out in either research or engineering, so when bridging the two fields, be honest about your limitations. To be truly interdisciplinary, you have to accept the areas in which you need to grow.

3. On that note, be patient in the face of discomfort. The bottom line is you’re going to have some hard nights figuring out some tough stuff. But it’s worth it. In the end, you’ll have expanded your horizons while making some amazing advances in both fields.

4. Have a passion for magic. The whole reason we keep our research and development teams integrated at Curalate is so the same people who work on the bleeding edge of research are also working on the product. If you don’t enjoy the process of seeing your ideas come to fruition—and how your products are being used—you won’t love this type of work.

5. You have to move fast. Because, at the end of the day, you’re fighting a war on two fronts: competing with world-class researchers and world-class engineers. Remember: they’re only focused on one job. To win, you have to move fast and make sh*t happen.

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The Author

Lou Kratz

Lou Kratz, Lead Research Engineer

Lou Kratz is the Lead Research Engineer at Curalate. He received his PhD in computer vision from Drexel University in 2012, and then got hit by the start-up bug in the best way. In addition to computer vision and machine learning, he is interested in optimization, signal processing, bocce, and Jeopardy. He lives in Philadelphia, which he uses as a primary subject for his Instagram account – at least, until his daughter was born.


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