Meta - Providence, RI

posted 2 months ago

Full-time - Mid Level
Remote - Providence, RI
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About the position

Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., is seeking a Software Engineer specializing in Machine Learning to join our innovative team. In this role, you will be responsible for researching, designing, developing, and testing operating systems-level software, compilers, and network distribution software that addresses massive social data and prediction problems. You will work on a variety of ranking, classification, recommendation, and optimization challenges, such as payment fraud detection, click-through or conversion rate prediction, and ads/feed/search ranking. Your expertise will contribute to the development of highly scalable systems and algorithms that leverage deep learning, data regression, and rules-based models. As a Software Engineer, you will be expected to suggest, collect, analyze, and synthesize requirements while identifying bottlenecks in technology, systems, and tools. You will develop solutions that significantly enhance efficiency and explore state-of-the-art deep learning techniques. Working closely with the engineering team, you will receive general instructions from your supervisor and code deliverables in tandem with your colleagues. This position allows for telecommuting from anywhere in the US, providing flexibility in your work environment. To succeed in this role, you will need a strong foundation in machine learning frameworks and experience in translating insights into actionable business recommendations. You will also be required to adapt standard machine learning methods to exploit modern parallel environments, including distributed clusters and GPU systems. This position offers a unique opportunity to contribute to the next evolution in social technology, as Meta moves beyond traditional 2D screens toward immersive experiences like augmented and virtual reality.

Responsibilities

  • Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
  • Work on a range of ranking, classification, recommendation, and optimization problems, such as payment fraud detection and click-through rate prediction.
  • Develop highly scalable systems, algorithms, and tools leveraging deep learning, data regression, and rules-based models.
  • Suggest, collect, analyze, and synthesize requirements and identify bottlenecks in technology, systems, and tools.
  • Develop solutions that enhance efficiency and leverage large amounts of data using state-of-the-art deep learning techniques.
  • Code deliverables in tandem with the engineering team under general supervision.
  • Adapt standard machine learning methods to exploit modern parallel environments, including distributed clusters and GPUs.

Requirements

  • Master's degree in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field.
  • Three years of work experience in the job offered or in a computer-related occupation.
  • Experience with machine learning frameworks such as PyTorch, MXNet, or TensorFlow.
  • Experience in machine learning, recommendation systems, computer vision, natural language processing, data mining, or distributed systems.
  • Experience in translating insights into business recommendations.
  • Proficiency with Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark.
  • Experience with scripting languages such as Perl, Python, PHP, or shell scripts.
  • Proficiency in Python, PHP, or Haskell.
  • Experience with relational databases and SQL.
  • Familiarity with Linux, UNIX, or other *nix-like operating systems, including file manipulation and shell scripting.
  • Experience in building highly-scalable performant solutions.
  • Knowledge of data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction.
  • Ability to apply algorithms and core computer science concepts to real-world systems.

Benefits

  • Bonus
  • Equity
  • Health benefits
  • Flexible work environment
  • Telecommuting options
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