Since 2006, Cafeyn’s group has been in a fast paced environment, acting in a market that is in constant evolution.
Our ambition is to become the go-to platform for access to information. Within the press sector and its digital transformation, our objective is to become a European champion of information streaming within the next five years.
We’ve strengthened our international presence thanks to the acquisition of Milibris & Blendle. With, for now, 180 Cafeyners and 6 countries, we are ready to create an information haven to empower people. We feel they deserve a press service that allows them to focus on quality information wherever they are. We believe that quality information deserves the best reading conditions. That’s why our products are designed to generate optimal comfort, maximize the ability to focus and improve the well-being of our readers.
We have the trust of international key distributors as Bouygues, Canal +, O2, Three…
Cafeyn in numbers?
Our values? Excellence, Kindness, Ambition, Honesty & Innovation!
You will join the Tech - Data team, you will closely work together with the Data Scientists and have a product view; models you build and bring into production are fuelling major parts of our application.
In addition you’ll work closely with the Data Engineers in the definition of data architecture, data flows (ETL) and implementation of, in a broad sense, data systems.
Our articles are the life force of the organization. We get multiple thousands of articles each day. From a recommendation perspective, the better we understand these articles, the more value we create for our readers and listeners based on these articles.
Our challenge is that we want to move towards a better understanding of these articles using state of the art NLP technologies. These include large scale pretrained models such as BERT and other transformer models. We need your expertise in setting up the environment for training and tweaking these models, and the infrastructure for bringing them into production. Having a better article understanding subsequently also allows us to work on improving our user understanding, and the recommendation algorithms, in which you’ll also play a big role.
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