Who we are
At Criteo, we connect 1.2 billion active shoppers with the things they need and love. Our technology takes an algorithmic approach to determining what user we show an ad to, when, and for what products. Our dataset is about 45 petabytes in Hadoop (more than 90 TB extra per day) and we take less than 10ms to respond to an ad request. This is truly big data and machine learning without the buzzwords. If scale and complexity excite you, join us.
Criteo AI Lab is pioneering innovations in online publishing and advertising. As the center of scientific excellence in the company, Criteo AI Lab in Grenoble, Paris, Palo Alto deliver both fundamental and applied scientific leadership through published research, product innovations and new technologies powering the company's products.
We are looking for outstanding machine learning research scientists whose skills span the entire spectrum of scientific research, i.e. data gathering/cleaning, modeling, implementation, publication and presentation.
Sample of Research Topics
- Click prediction How do you accurately predict if the user will click on an ad in less than a millisecond? Thankfully, you have billions of data points to help you.
- Recommender systems A standard SVD works well. But what happens when you have to choose the top products amongst hundreds of thousands for every user, 2 billion times per day, in less than 50ms?
- Auction theory In a second-price auction, the theoretical optimal is to bid the expected value. But what happens when you run 15 billion auctions per day against the same competitors?
- Explore/exploit It's easy, UCB and Thomson sampling have low regret. But what happens when new products come and go and when each ad displayed changes the reward of each arm?
- Game theory/Reinforcement learning How to find the optimal bidding strategy across multiple auctions? Can this be cast as a reinforcement learning problem in very high dimensions with unpredictable rewards.
- Offline testing/Metrics You can always compute the classification error on model predicting the probability of a click. But is this really related to the online performance of a new model? What is the right offline metric that predicts online performance?
- Optimization Stochastic gradient descent is great when you have lots of data. But what do you do when all data are not equal and you must distribute the learning over several hundred nodes?
What you'll do
- Understand and shape the product direction by contributing innovative ideas, specifically as a result of data mining and experimental data modeling
- Influence the strategic vision for the research team and Criteo at large.
- Identify new research opportunities at Criteo and lead the exploration of these ideas and pursue patents/publications where appropriate.
- Interact with other teams to define interfaces and understand and resolve dependencies
- Maintain world-class academic credentials through publications, presentations, external collaborations and service to the research community.
- Develop high-performance algorithms, test and implement the algorithms in scalable and product-ready code.
- Mentor team members, oversee the creation of technical documents and work towards establishing Criteo as a center of excellence in computational advertising.
Who you are
- PhD in Machine Learning or a related field along with at least five years of experience.
- Strong hands-on skills in sourcing, cleaning, manipulating and analyzing large volumes of data.
- Strong implementation experience with languages, such as Python, Perl, Ruby, Java, C#, Scala etc.
- Familiarity with Linux/Unix/Shell environments.
- Knowledge of Hadoop programming environments (e.g. Pig, Hive).
- Excellent track record in conducting and reporting results of original and collaborative research with publications.
- Prior experience in optimization of online advertising is a plus.
Criteo R&D Culture
Empowerment – We believe in hiring the best engineers in the industry and then letting them get on with what they do best – designing, coding and releasing state of the art software.
Mobility – In our Voyager program our engineers get to pick which team they want to work on for 2-4 weeks, boosting collaboration, networking and maybe even leading to switching teams.
Agility - We work in a fast pace environment where we build and release stuff frequently to deliver value soon and adapt to changes quickly.
Variety – We have many ways to get your code to production including our Hackathon, 10% projects, Voyager and more.
Multicultural – We have engineers from all over the world for you to interact and exchange ideas with.
Our culture keeps evolving, and you will be expected to contribute actively with new ideas to complement and enhance the existing programs that include frictionless internal mobility, 10% time, mentoring, technical talks, hackathons, conferences, etc.
We recognize that engineering culture is key for building a world-class engineering organization. Our core values are getting stuff done, collaboration and respect, code quality, striving for excellence, and having fun at what we do.
Do you want to know more about life in the R&D?
Youtube: R&D Criteo @ Europe
Our blog: http://www.criteolabs.com
At Criteo, we dare to be different. We believe that diversity fuels innovation and creates an energy that can be seen and felt all over Criteo. We champion different perspectives and are committed to creating a workplace where all Criteos are heard, feel a sense of belonging, and are treated with respect and dignity.
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