Mohamed Bin Zayed University of Artificial Intelligence

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ML Systems/AutoML Engineer (Center of Integrative Artificial Intelligence)

We are looking for talented, motivated full-time ML Systems and AutoML (automated machine learning) Engineers who can deliver consistently in a fast-paced and high-quality manner. You will be responsible for helping build robust, effective, and well-packaged modern ML-Systems and AutoML systems, as well as contributing to our open-source projects.


  • Collaborate with system architects, designers, and engineers to support the development of robust machine learning systems.
  • Contribute high-quality code and lead efforts in building open-source projects
  • Develop parallel programming techniques to simplify distributed ML programming.
  • Learn and implement state-of-the-art deep AutoML algorithms to support tasks such as hyperparameter optimization, neural architecture search, data augmentation, feature engineering, and more.
  • Assess and recommend technology choices and directions in consideration of cost-benefit trade-offs.
  • Communicate your work to a broader audience through talks, tutorials, and blog posts.

Minimum Qualifications

  • 2+ years of experience in one or more areas listed below:
    • AutoML areas such as hyperparameter tuning, architecture search or manual design, data preparation, augmentation, or feature engineering
    • Distributed systems
    • Network communication, or storage systems
  • Hands-on experience with at least one popular deep learning framework such as PyTorch and Tensorflow.
  • High-level engineering skills in Python and C++.

Preferred Qualifications

  • Master’s degree in Computer Science, Machine Learning, or related fields with 2+ years of industry/research experience, or Ph.D. degree in Computer Science, Machine Learning, or other relevant degrees.
  • Experience with model-based optimization (e.g. Bayesian optimization) methods or software frameworks.
  • Experience in deploying machine learning algorithms in resource-restricted environments such as mobile or embedded systems.
  • Experience in developing with Docker, Kubernetes, Ray, NNI, etc.
  • Experience in contributing to notable open-source ML software, such as TensorFlow, PyTorch, etc.
  • Publication (or submission) of a paper to machine learning or operating systems conferences.

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