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If you are an applied research engineer/scientist with a passion for working on massive semi-structured text and graph datasets, then the LinkedIn Data Team is the place for you. The ideal candidate will have domain experience (data mining, information retrieval, security data science, natural language processing, advanced statistics, and/or machine learning), a strong systems orientation, and experience in building data mining products. The work you put forth will directly impact and fuel LinkedIn's search relevance, ad targeting, information extraction, and recommendations.
As a Staff Software Engineer, you will be an agile architect in the design, development, and support of the most visible Internet-scale features and infrastructures at LinkedIn.
•Work with BIG data, crunching millions of samples for modeling data mining, recommendation, or search relevance solutions.
•Provide technical leadership, driving and performing best engineering practices to initiate, plan, and execute large-scale, cross functional, and company-wise critical programs. •Identify, leverage, and successfully evangelize opportunities to improve engineering productivity.
•BA/BS Degree in Computer Science or Machine Learning or related technical discipline, or 10+ years of related practical experience. •4+ years experience in software design, development, and algorithm related solutions. •4+ years experience programming experience in Java, C/C++.
•BS + 8 years of relevant work experience, MS + 7 years of relevant work experience, or PhD + 4 years of relevant work experience •Experience in designing and building infrastructure and web services at large scale. •Expertise in one or more of the following: machine learning, data mining, security data science, advanced statistics, information retrieval, or natural language processing.
•Experience with iterative, test-driven development. •Experience with configuration management (SVN, GIT, ant, maven, etc. ). •Experience with developing and designing consumer-facing data based products •Experience with Hadoop, Pig, or other MapReduce paradigms. •Knowledge of internals Lucene/SOLR or other information retrieval systems. •Published work in academic conferences or industry circles. Candidates may be invited to present a talk on their work as part of the interview process.
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