Machine Learning Engineer

Meta Meta · Big Tech · Menlo Park, CA

Machine Learning Engineer at Meta focused on developing and optimizing large-scale machine learning systems for social data prediction problems, including ranking, classification, and recommendation. The role involves applying advanced deep learning techniques and leveraging parallel computing environments to build highly scalable algorithms and tools.

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 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.
  3. Work on problems of large 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 bottlenecks in technology, systems, and tools.
  5. Develop solutions that iterate with a higher efficiency, efficiently leverage orders of magnitude more data, and explore state-of-the-art deep learning techniques.
  6. Demonstrate strong engineering skills and require minimal guidance on engineering craft.
  7. Apply advanced machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).

Skills

Required

  • Machine Learning Frameworks: PyTorch, MXNet, or Tensorflow
  • Machine Learning Algorithms and their applications: recommendation systems, computer vision, natural language processing, or data mining
  • Translating insights into business recommendations
  • Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
  • Deep Neural Networks
  • Probability theory
  • Linear Algebra
  • Calculus
  • Data Analysis
  • Scrum
  • Kanban
  • C
  • C++
  • JavaScript
  • Perl
  • Python
  • PHP
  • shell scripts
  • Relational databases and SQL
  • Code editors (VIM or Emacs)
  • revision control systems (Subversion, GIT, or Perforce)
  • Linux, UNIX, or other *nix-like OS
  • Distributed systems
  • Data structures and Algorithms

Nice to have

  • experience working on a range of ranking, classification, recommendation, and optimization problems
  • 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)

What the JD emphasized

  • massive social data
  • prediction problems
  • ranking
  • classification
  • recommendation
  • optimization problems
  • highly scalable systems
  • deep learning
  • state-of-the-art deep learning techniques
  • advanced machine learning methods
  • distributed clusters
  • multicore SMP
  • GPU

Other signals

  • massive social data
  • prediction problems
  • ranking, classification, recommendation, and optimization problems
  • highly scalable systems, algorithms and tools
  • deep learning
  • state-of-the-art deep learning techniques
  • advanced machine learning methods
  • distributed clusters, multicore SMP, and GPU