Many of the best engineers have a strong interest in the literature, even if things are not completely understood theoretically. Good engineers provide strong j

Many of the best engineers have a strong interest in the literature, even if things are not completely understood theoretically. Good engineers provide strong justification for tuning hyperparameters based both on intuition and theory. The same goes with suggesting architectures -- the best people don't guess, but they can debate the theoretical purpose of each element and how tuning it may affect the outcome. If something isn't completely understood, they may code small experiments and/or work with the math to better understand its potential impact. 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 https://www.anthropic.com/research).