MINIMUM QUALIFICATIONS

• Bachelor’s degree (or foreign degree equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field and 2 years of experience in the job offered or in a computer-related occupation.
• Experience must include 2 years of experience in the following:
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• Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow

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• Machine learning, recommendation systems, ranking 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 including file manipulation and simple commands

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• Building highly-scalable performant solutions

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• Distributed systems including sharding, consistency, and availability and

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• Data structures and algorithms.
  
  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 classification 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).