eBay NYC is looking to hire a rock star Research Engineer who can help build the future of personalized recommendations in eBay. You will be part of a combined data science/engineering team, which is responsible for all merchandizing and personalization algorithms for eBay marketplaces. You will have a chance to tackle some of the most demanding algorithmic problems eBay is facing, building recommendations engines that serve around a billion recommendations to 100M users per day.
We are using state of the art Big Data infrastructures like hadoop (through cascading and Scalding) and Cassandra, and employ both Java and Scala to build our scalable real-time platform.
eBay currently employs one of the largest hadoop clusters in the industry, containing thousands of nodes and tens of thousands of processing cores. All this computing power is used to analyze over 30 Petabytes of data in order to gain better insight on how to best serve users.
While we stand firmly on theoretical and mathematical roots, we see great value real world, creative and pragmatic solutions.
Job Description:
Research and implement machine learning models for prediction, personalization and recommendation
Drive analysis and insights to the impact of newly developed features
Engineer and build large scale data crunching processes
Collaborate with database architects, other software engineers, QA and operations teams to deliver real time distributed recommendation serving platforms
BSC/MSC in computer science/engineering from a leading institute
Relevant science background in the areas of machine learning/information retrieval/recommender engines
Hands on experience with hadoop. Use of cascading and Scalding - big advantage.
Hands on experience with machine learning/information retrieval libraries and platforms (examples - Matlab, R, Mahout, LibLinear, LibSVM, Solr, Elastic Search)
Strong backend engineering background, ideally using Java and/or Scala
Motivated, self-driven, comfortable working in agile development environment
Versed in agile development coding practices (TDD, CI) and supporting technologies (GIT, Jenkins)
BSC/MSC in computer science/engineering from a leading institute
Relevant science background in the areas of machine learning/information retrieval/recommender engines
Hands on experience with hadoop. Use of cascading and Scalding - big advantage.
Hands on experience with machine learning/information retrieval libraries and platforms (examples - Matlab, R, Mahout, LibLinear, LibSVM, Solr, Elastic Search)
Strong backend engineering background, ideally using Java and/or Scala
Motivated, self-driven, comfortable working in agile development environment
Versed in agile development coding practices (TDD, CI) and supporting technologies (GIT, Jenkins)
source: http://m.dice.com/m2/servlet/MobileController?op=1003&dockey=2a458d332395e922ae5137137f14a766
