MINIMUM QUALIFICATIONS

• Requires a Master’s degree (or foreign degree equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or a related field.
• Requires completion of a university-level course, research project, internship, or thesis in the following:
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• Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow

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• Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems

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• Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark

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• Scripting languages: Perl, Python, PHP, or shell scripts

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• C, C++, C#, or Java

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• Relational databases and SQL

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• Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)

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• Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction

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• Applying algorithms and core computer science concepts to real world systems as evidenced by recognizing and matching patterns from different areas of computer science in production systems and

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• Distributed systems.
  
  For those who live in or expect to work from California if hired for this position.

• Research, design, develop, and test systems-level software for massive social data and prediction problems.
• Have industry experience working on a range of ranking, classification, recommendation, and optimization problems, eg payment fraud, click-through or conversion rate prediction, click-fraud detection, ads/feed/search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection.
• Working on problems of moderate scope, develop highly scalable systems, algorithms and tools leveraging deep learning, data regression, and rules based models.
• Suggest, collect, analyze and synthesize requirements and bottlenecks in technology, systems, and tools.
• Develop solutions that operate at high efficiency for 1B+ users, efficiently leverage large scale data, and explore state-of-the-art deep learning techniques.
• Receiving general instruction from supervisor, code deliverables in tandem with the engineering team.
• Adapt standard machine learning methods to best exploit modern parallel environments (eg distributed clusters, multicore SMP, and GPU).