Can you l2 normalize word2vec vectors for density clustering?
You can do DBSCAN, OPTICS, HDBSCAN with cosine similarity - they do not expect or require Euclidean distances.
Yes, one version of cosine distance corresponds nicely to Euclidean distance of L2 normalized vectors. So there is some theoretical support to L2 normalize the vectors and try Euclidean then.
Given how word2vec is trained, I don't think Euclidean nor cosine distance is the right thing to do. Instead, use the raw dot product as a similarity measure (beware that it is not limited to [0;1] but allows negative similarity - values may be too spread out to use them intuitively).
t-SNE, which was discussed in the comments and another answer, also relies on distances to the nearest neighbors. So it does suffer from the same problem - cosine, Euclidean, dot product? The fast ELKI implementation does support cosine distance as well as Euclidean, but you appear to be set on Python already. Also, if you have a large vocabulary, t-SNE may give you some scalability trouble. The standard algorithm will need O(n²) memory and time.