Software Engineer, Machine Learning

Meta Meta · Big Tech · New York, NY

Software Engineer, Machine Learning at Meta focused on developing and testing operating systems-level software, compilers, and network distribution software for massive social data and prediction problems. The role involves working on classification and optimization problems using deep learning, data regression, and rule-based models to build highly scalable systems and algorithms.

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. Have industry experience working 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. Working 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.
  6. Receiving general instruction from supervisor, code deliverables in tandem with the engineering team.
  7. Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).

Skills

Required

  • Machine learning
  • recommendation systems
  • ranking systems
  • computer vision
  • natural language processing
  • data mining
  • distributed systems
  • C++
  • Java
  • Python
  • SQL
  • Linux/UNIX

Nice to have

  • C#
  • Haskell
  • Perl
  • PHP
  • shell scripts
  • VIM
  • Emacs
  • Subversion
  • GIT
  • Perforce

What the JD emphasized

  • massive social data and prediction problems
  • highly scalable systems, algorithms and tools
  • state-of-the-art deep learning techniques
  • orders of magnitude with a higher efficiency
  • orders of magnitude and more data

Other signals

  • Develop solutions that iterate orders of magnitude with a higher efficiency
  • efficiently leverage orders of magnitude and more data
  • explore state-of-the-art deep learning techniques