Is the theory good enough for the practical applications? (by practical I don't mean large scale projects that Google or OpenAI does. More of just small companies who seek to apply established methodologies on their own dataset). If yes, then do people actually do this? If no, can I have more of your thoughts on why?
Beyond this, optimizing models requires a strong understanding of the math behind them. This provides crucial insights, for example that the attention key bias does not affect the attention weights. In industry, engineers might read a paper about a new activation function. They will probably wonder whether there is (theoretical) justification for how it might affect training time or be computationally efficient in the company's architecture. A theorist would be a great fit here.
Some higher-level research could have some commercial application. For example, there are a few papers showing near-LLM performance (for certain things) attained by only searching datasets.
Ultimately, you will be successful if you can find where your skillset overlaps with what the company needs. Sometimes there is a research division which needs exactly what you research. Other times, you may need to work on things that aren't an exact fit but still utilize your skills. But as of right now, it seems that a successful theorist with coding ability would be in demand (e.g. https://www.anthropic.com/research).
But if your question is whether DL theorists are employable as DL theorists, the answer is again - sure, but you have to be really good at it.
Unless you are implementing your theories yourself, I'm going to go ahead and guess "no" on this one.