  Wildlife traffickers are increasingly carrying out their activities in
cyberspace. As they advertise and sell wildlife products in online
marketplaces, they leave digital traces of their activity. This creates a new
opportunity: by analyzing these traces, we can obtain insights into how
trafficking networks work as well as how they can be disrupted. However,
collecting such information is difficult. Online marketplaces sell a very large
number of products and identifying ads that actually involve wildlife is a
complex task that is hard to automate. Furthermore, given that the volume of
data is staggering, we need scalable mechanisms to acquire, filter, and store
the ads, as well as to make them available for analysis. In this paper, we
present a new approach to collect wildlife trafficking data at scale. We
propose a data collection pipeline that combines scoped crawlers for data
discovery and acquisition with foundational models and machine learning
classifiers to identify relevant ads. We describe a dataset we created using
this pipeline which is, to the best of our knowledge, the largest of its kind:
it contains almost a million ads obtained from 41 marketplaces, covering 235
species and 20 languages. The source code is publicly available at
\url{https://github.com/VIDA-NYU/wildlife_pipeline}.
