As the largest online marketplace for hourly jobs in the US, Snag strives to connect millions of job seekers with part/full time, hourly and on-demand employment opportunities. Snag started building its learning-to-rank (LTR)-based search system using the Elasticsearch learning-to-rank plugin in 2017 and has switched all of its user queries to LTR by mid-2018, generating significant lift to overall search quality. While fine-tuning and maintaining the LTR system over the past 12 months, our team has come to the realization that continued success of the LTR system requires not only a great ranking model, but also an ecosystem of intelligent metadata services and reliable data infrastructure. This talk is a collection of examples about the growing pains and remedies of iterating LTR beyond v1.0 at Snag. To start, we will address a few nuances of LTR as a machine-learning problem, e.g. high sample complexity, potential biases from training data, limitations of BM25-based features, incorporation of user preferences, evaluation metrics to please both human users and SEO bots, etc. Then, we will present a few of our newest developments to supplement the current LTR system, including our posting deduplication services, job title normalization services, and architectural designs of our next-generation signal platform and posting enrichment pipeline.