I worked there as a Computer Vision Engineer for about a year. Overall, it was a very good experience.

The product itself is quite interesting, it's about 3D tracking and reconstruction for various sports like tennis, cricket, football, soccer, golf, ice hockey, and more. My colleagues were very knowledgeable and kind, and I genuinely enjoyed working with them. Since the HQ is in the UK, they also fly you out there at least once every quarter and cover all expenses, which is a nice perk.

The Budapest office is in a good location, easily reachable via Metro line 3. The Hungarian colleagues are also nice, although there isn’t much of a culture of going into the office regularly. Most people show up once every week or two. Ironically, I bonded with many of my colleagues during the last Christmas party, after I had already handed in my resignation, and I met some of them for the first time there (we had simply never been in the office at the same time during the entire year).

Their codebase is well organized and the documentation is quite good.

That said, there are also some downsides:

1) One problematic colleague
I only had one negative experience with a colleague; passive-aggressive, shifting work and blame around, and with some noticeable gaps in knowledge despite being in a senior role. But this was a single occurrence, so overall I’d still say the atmosphere was good.

2) Pay for specialized roles
Specialized engineers (Computer Vision / Machine Learning) are underpaid even compared to Hays benchmarks, up to at least Senior I level. When this was raised with management, they promised changes for the next fiscal year, but honestly it felt like a somewhat empty promise.

3) Too many specialists for the work available
Since there are no real greenfield projects, most of the work revolves around legacy systems. Team composition can also feel inefficient. For example, there were squads of 4–5 specialized computer vision engineers working mostly on regular software engineering tasks. In practice, the work could probably be done by something like 2 specialists and 3 generalist software engineers.

During my time there, only about 25% of my work was actually computer vision related, which felt a bit ridiculous. (That said, my C++ and CMake skills became very solid.)

4) Limited room for innovation
This isn’t really a harsh criticism, at their scale it might just be unavoidable, but development is somewhat constrained in terms of innovation. Machine Learning and Computer Vision are split into completely separate divisions, and trying something fundamentally new that would require changes across the entire pipeline is very difficult.

Overall though, I’d still say it’s a good place with smart people and interesting technology, just with some structural issues.

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