Search Engineering is a new team comprised of highly skilled and senior engineers; experts in the search domain. We are building a global Search Component in our Paris Hub which we will quickly take on a journey from MVP to 100 +million unique users a month as we integrate the product with our marketplaces all over the world. Our teams are very autonomous and self organising; they are empowered to define the stack, approach to agile and architecture as a collective rather than from the top down.

Responsibilities:

• Selection of the right machine learning algorithm for business goals
• Build scalable machine learning tools, distributed clusters and models for search (MLR)
• Experiment with different models and assess their potential in offline evaluations and by setting up A/B tests
• Collaborate in cross-functional teams consisting of product managers, data engineers and analysts to build a great search product that correspond to the needs of our market places
• Contribute to the end-to-end deployment of machine learning models
• Popularize search initiatives via Medium posts and meetup talks and our internal community

PhD or Masters degree (or equivalent) in computer science/mathematics/physics or a relevant scientific field

Engineer by heart, but passionate about machine learning.

+5 years experience in industry in a similar role

• Extensive experience applying machine learning modelling to create data products
• Being able to communicate your findings in a concise way to a technical and non-technical audience
• Comfortable working in an iterative incremental framework
• Exposure to Search technologies like Lucene, Solr and Elastic Search
• Knowledge in Machine Learning, Natural Language Processing and statistical techniques such as query expansions for search

What would be a plus:
Experience with large data sets, distributed computing, and Apache Spark or Hadoop/MapReduce
Experience with streaming tools such as Kafka, Spark Streaming, Flink
Experience with AWS and/or other cloud providers

1. Practical case: real case ranking and NLP
2. Machine Learning Fundamentals: Machine Learning concepts
3. System Architecture: how to put models in production
4. Culture fit: culture fit with the team

Welcome on board!