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, developing 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 iterate orders of magnitude with higher efficiency, efficiently leverage orders of magnitude and more data, and explore state-of-the-art deep learning techniques.
Receiving general instruction from a supervisor, and 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).
Requirements & Skills:
Requires a Master’s Degree in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or a related field and 2 years in the job offered or in a related position.
Requires 24 months of experience involving 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
Building 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 and Distributed systems.
