MINIMUM QUALIFICATIONS

• Requires a Master’s degree (or foreign equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field
• Requires completion of a graduate-level course, research project, or internship involving each of 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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• Translating insights into business recommendations

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

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• Developing and debugging in C/C++ and Java

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• Scripting languages such as Perl, Python, PHP, or shell scripts

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• C, C++, C#, or Java

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• Python, PHP, or Haskell

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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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• Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting

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• Build highly-scalable performant solutions

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

• Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
• Have industry experience working on a range of ranking, classification, recommendation, and optimization problems, e.g.
• 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 bottleneck in technology, systems, and tools.
• Develop solutions that iterate orders of magnitude with a higher efficiency, efficiently leverage orders of magnitude and more 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 (e.g. distributed clusters, multicore SMP, and GPU).