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Senior Machine Learning Engineer, Pipelines 282 views

As a Machine Learning Engineer in DataRobot, you will work on their machine learning platform and actively contribute to the automation of data science best practices and the development of their state-of-the-art preprocessing, modeling and reporting capabilities.

They are looking for talented people with excellent engineering skills and deep knowledge of Machine Learning who can analyze problems, develop innovative generalized solutions, and implement them for real-world use on top of their platform.

DataRobot is based around delivering best-in-class data science solutions and this position provides the opportunity to build key data science components of their system.

Responsibilities

  • Design and build next-generation machine learning frameworks
  • Build interfaces that make it easy to write good data-science code and hard to take dangerous shortcuts
  • Integrate machine learning algorithms with other applications and services

Main Requirements

  • Recommended background: 5+ years of combined Python engineering and machine learning experience
  • Experience writing maintainable, testable, production-grade Python code
  • Understanding of different machine learning algorithm families and their tradeoffs (linear, tree-based, kernel-based, neural networks, unsupervised algorithms, etc.)
  • Good command of scientific Python toolkit (numpy, scipy, pandas, scikit-learn)
  • Understanding of time, RAM, and I/O scalability aspects of data science applications (e.g. CPU and GPU acceleration, operations on sparse arrays, model serialization and caching)
  • Software design and peer code review skills
  • Experience with automated testing and test-driven development in Python
  • Experience with Git + GitHub
  • Comfortable with Linux-based operating systems

Desired Skills

  • Experience with large-scale machine learning (100GB+ datasets)
  • Experience with deep learning libraries and frameworks (TensorFlow, Keras, PyTorch, etc.)
  • Competitive machine learning experience (e.g. Kaggle)
  • Previous experience of deploying and maintaining machine learning models in production

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