What is the complexity of Hamming nearest neighbor to a subspace ...? Suppose that $F_2$ denotes the field with $2$ elements. We are given $m$ vectors $\{x_1, \ldots, x_m\}$ in $F_2^d$ which are a basis for a subspace $W$. 

Suppose we have a vector $v \in F_q^m$, and we want to find a $w \in W$ that is closest to $v$ in the Hamming metric.

What is the complexity of this problem, as a function of $md$?

1) It's clear that there is an algorithm taking exponentially many steps. $W$ has $2^m$ elements, each of which takes $d$ bits to describe. So we just search through them.

2) It's unclear to me if this problem is even in $NP$. I can verify in polynomial time whether one vector in $W$ is closer to $v$ than another vector in $W$, but I do not know how to verify if one is the closest (or minimizes the distance). I suspect that it is impossible to do. Can anyone outline or refer me to a proof that this problem is not in $NP$?

3) If it is in $NP$, then is it $NP$-hard?

I guess this is well known, and presumably already discussed on this webpage. I poked around for a bit and couldn't find it, however. A reference would be welcome.
Your problem is known as the nearest codeword problem, and it is NP-hard to approximate. See for example lecture notes of Madhu Sudan. The way to make this problem an NP-problem is to ask whether the distance is at most a given distance. Regarding algorithms, I suggest taking a look at a paper of Alon, Panigrahy and Yekhanin, Deterministic Approximation Algorithms for the Nearest Codeword Problem.