One hot encoding with too many features (~ 10,000)
I think you are on the right track. What you are looking for is what is known as fixed effects in econometrics/statistics. Your have a model in which features X determine outcome y (so y = bX + u). On top of that you have unobserved heterogeneity (each individual employee) in your model. For each employee, the relation y &lt;-&gt; X may be a little different (fixed effect per employee), so that your model becomes y = b1X + b2Z + u, where Z denotes employees.

See here for an overview and here for an R implementation.

You can solve this by adding one dummy/indicator/one-hot per employee (known as least squares dummy variable fixed effects). So technically speaking, each employee gets his/her own intercept in the model. Thus the Z above is a massive matrix of one-hot encoded employee labels. 

There are solutions in Stata (areg) and R (felm) which "absorb" the massive dummy variable set, so that it does not bother you when looking at the regression coefficients. If you are not interested in statistical inference (but only in making predictions), you will not look at the coefficients anyway.

I don't know the datails of your data (i.e. observations per employee), but from what you say, adding one dummy (aka one-hot feature) per employee would be the right way to go to control for unobserved heterogeneity of the employees.