Software Engineer

Meta Meta · Big Tech · New York, NY

Software Engineer role at Meta focused on developing and optimizing large-scale systems for social data and prediction problems. The role involves applying deep learning, data regression, and rule-based models to areas like fraud detection, recommendation systems, and spam detection, with a strong emphasis on adapting ML methods for parallel environments (distributed clusters, GPUs). Requires a Master's degree and 2 years of experience with ML frameworks and distributed systems.

What you'd actually do

  1. Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
  2. Utilize industry experience to work 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.
  3. Work on problems of moderate scope, develop highly scalable systems, algorithms and tools leveraging deep learning, data regression, and rules based models.
  4. Suggest, collect, analyze and synthesize requirements and bottleneck in technology, systems, and tools.
  5. 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.

Skills

Required

  • Master’s degree (or foreign equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field
  • 2 years of experience in the job offered or in a computer-related occupation
  • Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
  • Machine learning, 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
  • Build 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

What the JD emphasized

  • develop highly scalable systems, algorithms and tools leveraging deep learning
  • Adapt standard machine learning methods to best exploit modern parallel environments
  • Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
  • Machine learning, data mining, or distributed systems

Other signals

  • develop highly scalable systems, algorithms and tools leveraging deep learning
  • Adapt standard machine learning methods to best exploit modern parallel environments
  • Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
  • Machine learning, data mining, or distributed systems