- 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).
- Maintain the production presence of the Instagram Oncall and SEV handling, to ensure system stability and control the damage of the production issue.
- Leverage the ads industry insights and understanding, bring production innovation into Instagram Ads to unlock new ads demand from Meta Advertisers.
- Establish the high-quality documentation of the Instagram ads ranking model and delivery system, conduct information sharing and tech review session to reduce key-man risk and lower the barrier for new team member onboarding.
- Requires a Master’s degree in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Finance, Physics, or related field and three years of work experience in the job offered or in a computer-related occupation. Requires three years of experience in the following:
- Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
- Machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems
- Translating insights into business recommendations
- Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
- Developing and debugging in C, C++, and Java
- Scripting languages: 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 including file manipulation and simple commands
- Distributed systems including sharding, consistency, and availability
- Building highly-scalable performant solutions
- Data structures and algorithms.
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