Machine Learning Engineer

Requires a Bachelor's degree (or foreign equivalent) in Computer Science, Engineering, Applied Sciences, Mathematics, Physics or related field.
Requires completion of a university-level course, research project, internship, or thesis in the following:
1. Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
2. Machine Learning Algorithms and their applications: recommendation systems, computer vision, natural language processing, or data mining
3. Translating insights into business recommendations
4. Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
5. Deep Neural Networks
6. Probability theory, Linear Algebra, Calculus, Data Analysis
7. Understanding of agile methodologies such as: Scrum, Kanban
8. Developing and debugging in C, C++, and Java
9. Scripting languages: Perl, Python, PHP, or shell scripts
10. Relational databases and SQL
11. Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)
12. Linux, UNIX, or other *nix-like OS including file manipulation and simple commands
13. Distributed systems, including sharding, consistency, and availability
14. Building highly-scalable performant solutions
15. Data structures and Algorithms

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.
Work on problems of large scope, develop 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 with a higher efficiency, efficiently leverage orders of magnitude more data, and explore state-of-the-art deep learning techniques.
Demonstrate strong engineering skills and require minimal guidance on engineering craft.
Apply advanced machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).

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