Petuum’s mission is to unlock human productivity and well-being by advancing the limits of AI technology standards and engineering to build trustworthy AI products. The Petuum team is looking for talented, motivated full-time ML Systems and AutoML (automated machine learning) Engineers who are able to deliver consistently in a fast-paced and high-quality manner. You will be responsible for helping build robust, effective, and well-packaged modern machine learning systems, as well as contribute to our CASL 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 Petuum’s open-source CASL projects such as AdaptDL, AutoDist, Tuun.
·      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: 
·      Hands-on experience in one or more areas listed below: 
o  AutoML areas such as hyperparameter tuning, architecture search or manual design, data preparation, augmentation, or feature engineering
o  Distributed systems 
o  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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