Machine Learning Engineer: Scalable Production ML

Machine Learning Engineer Responsibilities

• 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, such as 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.
• Collaborate with Dev Ops team to Develop and deploy machine learning models into production environments and ensure smooth operation of machine learning models in production.
• Work with data engineers to design and implement data pipelines for large-scale machine learning tasks.
• Collaborate with cross-functional teams to integrate machine learning models into larger systems.
• Participate in code reviews and ensure that all solutions are aligned with industry standards and best practices.
• Work with data scientists to analyze and interpret model performance metrics.

Minimum Qualifications

• Requires a Master’s degree in Management Information Systems, Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, 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, Map Reduce, Sawzall, Bigtable, or Spark
• Scripting languages:
  Perl, Python, PHP, or shell scripts
• 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 as evidenced by file manipulation, advanced commands, and shell scripting
• 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 as evidenced by recognizing and matching patterns from different areas of computer science in production systems
• Distributed systems.