MINIMUM QUALIFICATIONS

• Master’s degree (or foreign equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field
• Completion of one graduate-level coursework, research project or internship must include experience in the following:
• Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
• Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
• Translating insights into business recommendations
• Hadoop/HBase/Pig or MapReduce/Sawzall/Bigtable/Spark
• Developing and debugging in C/C++ and Java
• Scripting languages such as Perl, Python, PHP, or shell scripts
• C, C++, C#, or Java
• Python, PHP, or Haskell
• Relational databases and SQL
• Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)
• Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting
• Build highly-scalable performant solutions
• Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction
• 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
• Distributed 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 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).