I'm using Solr for https://www.findlectures.com, but I think Vespa looks interesting - lets you store feature vectors in the index, so you can do neat things to incorporate ML algorithms in ranking.
Feature vectors do tend to get incorporated in relevance tuning (regardless of the engine), but from what I've heard of Vespa, features (and ML in general) are first-class citizens, whereas with Elasticsearch and Solr, text statistics are your first-class citizens, and you're adding in additional features and integrating ML at the periphery.