Optimizely is the world's leader in customer experience optimization, allowing businesses to dramatically drive up the value of their digital products, commerce and campaigns through its best in class experimentation software platform. By replacing digital guesswork with evidence-based results, Optimizely enables product and marketing professionals to accelerate innovation, lower the risk of new features, and drive up the return on investment from digital by up to 10X. Over 26 of the Fortune 100 companies choose Optimizely to power their global digital experiences. Optimizely’s impressive customer list includes eBay, FOX, IBM, The New York Times and many more global enterprises.
At Optimizely, we have built the world’s first Experimentation Platform that enables Testing and Personalization across all channels such as Web, Mobile, Server-side, and OTT through core components such as Analytics, Targeting, and Recommendations.
Our teams have built sophisticated infrastructure that processes billions of events per day, enriches them via stream processing, aggregates and stores them efficiently, supports large scale performant queries, and serves targeting information and recommendations in real-time. Our engineers speak at conferences, write blog posts to share their work, and contribute back to open-source projects.
We are looking for a talented engineer to help us build machine learning features and products to automate our customers’ journeys through complex experimentation hypotheses and analyses. This is a unique opportunity to work on a small team and own a large part of Optimizely’s machine learning strategy. Our core challenges are to build the right set of features that enable 1:1 personalization and automated optimization for our customers.
You will work on machine learning features and products such as Recommendations, Audience Discovery, User Scoring and Prediction, and Automated Optimization
You will build Prediction Services that rely on offline and online data integration, appropriate feature selection, and model training
You will build systems and services using batch and stream processing spanning technologies such as Hadoop, Samza, and Spark in languages such as Java and Scala
You will understand the scalability and performance tradeoffs among various ML techniques and appropriately build ML systems as we move from prototype to production
You will coordinate and work with other application product teams to implement Machine Learning features end to end throughout our platform
You have practical machine learning experience that allows you to develop sound approaches for problems such as contextual decision making, clustering, content targeting and optimization, ranking, prediction, and classification
You have experience with distributed systems to adapt machine learning approaches from simulation to prototypes to production; in particular you have hands-on experience with at least some of following: Hadoop, Samza, Spark, HBase, SQL DBs
You have 3 to 5 years of experience doing this and are looking for your next big challenge
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