Software Engineer in Machine Learning/


Your mission We are searching for great machine learning engineers to join the team responsible for: · Extending Criteo's large scale distributed machine learning library (e.g., implementing new distributed and scalable machine learning algorithms, improving their performance) · Building and improving prediction models for ad targeting; proving the business value of the changes and deploying them to production · Gathering and analyzing data, performing statistical modeling You'll have the opportunity to work on highly challenging problems with both engineering and scientific aspects; for example: · Click prediction:ÂHow do you accurately predict in less than a millisecond if the user will click on an ad? To qualify for this mission, you need: · MS degree in Computer Science or related quantitative field with 3 years of relevant experience or Ph.D degree in Computer Science or related quantitative field · Good understanding of the mathematical foundations behind machine learning algorithms · Great coding skills. Ability to write high performance production-grade code · Experience in one or more of the following areas: large-scale machine learning, recommender systems, or bandit algorithms ÂBonus points · Extensive experience in building and extending large scale production machine learning systems · Experience working with: Hadoop/Yarn, Spark · Experience in online advertising · Fluent in English About Criteo [CTRO] Criteo delivers personalized performance marketing at an extensive scale. A few figures: â 15 datacenters (8 with computing capacity 7 dedicated to network connectivity) Âacross US, EU, APAC â More than 15K servers, running a mix of Linux and Windows â One of the largest Hadoop clusters in Europe with close toÂ40PB of storage and 30.000 cores â â 30B HTTP requests and close to 3B unique banners displayed per day â Close to 1M HTTP requests per second handled during peak times â Â40Gbps of bandwidth, half of it through peering exchanges We recognize that engineering culture is key for building a world-class engineering organization.