Software Engineer, Machine Learning Responsibilities

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).

Minimum Qualifications

Bachelor’s degree (or foreign equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics or a related field. Requires completion of one university-level course, one research project, or one internship involving the following:
Machine learning, recommendation systems, ranking systems, computer vision, natural language processing, data mining, or distributed systems
Translating insights into business recommendations
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 including file manipulation and simple commands
Building highly-scalable performant solutions
Distributed systems including sharding, consistency, and availability and
Data structures and algorithms

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