- Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
- Work on problems of diverse 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.
- 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).
- Requires a Bachelor’s degree (or foreign degree equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field, and five (5) years of progressive, post-baccalaureate work experience in the job offered or in a computer-related occupation. Experience must include 5 years of experience in the following:
- 1. Filesystems, server architectures, and distributed systems.
- 2. Machine learning, recommendation systems, pattern recognition, data mining, or artificial intelligence.
- 3. Translating insights into business recommendations.
- 4. Hadoop/HBase/Pig or MapReduce/Sawzall/Bigtable/Spark.
- 5. Developing and debugging in C/C++ and Java.
- 6. Scripting languages such as Perl, Python, PHP, or shell scripts.
- 7. C, C++, C#, or Java.
- 8. Python, PHP, or Haskell.
- 9. Relational databases and SQL.
- 10. Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce).
- 11. Linux, UNIX, or other *nix-like OS including file manipulation and simple commands.12. Building highly-scalable performant solutions.
- 13. Designing scalable distributed systems with established partition tolerance, consistency, and availability guarantees.
- 14. Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction.
- 15. 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.
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