I'm not sure how this gets around the circularity arguments though, since you never get to evaluate the job performance of someone you selected out already. Only the tiny fraction of coders that make it past the initial test get evaluated, which could serve to reinforce the potential biases rather than ameliorate them.
The one case in which this would work is if they hired a number of coders that didn't work out well, and could add or update a feature as a negative predictor of job success.
I'm assuming that they're not at the scale of a larger company with thousands of engineers, and that the observations going into a regression model are relatively sparse. If this is a startup with a 20 hires, I'd be surprised if there was much to do to refine the model after a round or two of evaluations, but would be excited to learn otherwise.