We do application development with a data focus in general and I have always been trying to push the edge of what is possible in terms of high performance computing with the JVM. Lots of times I would just like to use some system or library from Julia with a lot less drama that shelling out or write something in Julia in order to get a lot more perf out of some bespoke piece of code.
The JVM is great because it handles really generic code well and it has a very powerful JIT and garbage collector. My opinion is that this is what makes Clojure possible as that style of functional programming generates a lot of ephemeral garbage as a side effect.
By comparison, I like the tradeoffs that Julia has made; much weaker GC and more emphasis on numerics and powerful (but simply expressed) compilation of the type that is impossible using the JVM regardless of language.
So they seem very complimentary. Many non-numeric algorithms can be expressed with sort of radical simplicity in Clojure and will get sufficient performance but there is a growing need for really great numerics support and instead of attempting to build out the foundation in Java where your performance bar is fairly low for this sort of thing I look to bring the best in the world in range of your normal Clojure programmer. Julia, IMHO, is head and shoulders above everything else including Python, Nim, and R in language design and focus on great numerics. It is setting itself up to be the one true Fortran replacement :-).
So that is the why.
I think at this point libjulia-clj is working fine for us although it is crashing when I mess with NextJournal but that is a whole other can of worms. It is easy for me at this point to pull a piece of code, write it in Julia and call it getting a major speed boost like what is demonstrated by the README on libjulia-clj all without changing the larger systems we have built.
It is also easy for us to use Julia implementations of algorithms such as UMAP and BayesianOptimization. The other option, use Python, also works however I do not enjoy writing Python myself nor do I enjoy the systematic brittleness of the Python ecosystem. In the long run I see Julia as a much better option.
So I am satisfied that for us, Julia is now an option to use in our application development pathways and most importantly when we are doing research and exploring systems.
Another example of research along these lines is our support of the tvm compiler.
I think that probably (more than) answers your question but let me know if not :-).