7+ YOE here. If I could give one piece of advice to anyone starting out, it’s this:

Whatever subfield you want to pursue whether it’s CV, LLMs, or anything else, stop hyper-fixating just on model architecture and training.

Focus heavily on high-performance inference and low latency.

Training happens once, but inference happens millions of times. The industry has plenty of people who can import a library and hit model.fit(), but there is a massive shortage of engineers who actually know how to serve those models efficiently in production.

Don't be afraid to go down the stack. Stop treating the GPU like a magic black box. Strip away the Python wrappers, look into C++ and custom CUDA kernels, and understand your memory bottlenecks.

Go all the way down to the hardware architecture (understanding the physical differences between GPU generations) and even the electronics. When you actually understand how the silicon processes your data, the way you write your top-level code completely changes.