Software Engineer – Machine Learning

Software Engineer – Machine Learning

Requirements

• Requires a Master’s degree (or foreign degree equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field.
• 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, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
• 3. Translating insights into business recommendations
• 4. Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
• 5. Developing and debugging in C, C++, and Java
• 6. Scripting languages: 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 as evidenced by file manipulation, advanced commands, and shell scripting
• 12. Build highly-scalable performant solutions
• 13. Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction
• 14. 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
• 15. Distributed systems

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 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, 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).
• Build ranking algorithms for ad products to drive advertising value while delivering relevant and quality ads to users on Meta messaging platforms.
• Optimize the performance of ads ranking models using state of the art machine learning and deep learning techniques.
• Perform hypothesis driven experimentation along with careful data analysis to validate the model improvements.

Preferred Qualifications

No preferred qualifications provided.