The research programmer will work with faculty, PhD students, and other group members to develop production implementations of several machine learning algorithms being developed by the group. This will include writing production versions of the algorithms themselves, developing Python APIs that provide these machine learning algorithms as services to other applications, and integrating the work within a large software stack. Essential duties include writing and testing production Python machine learning code, managing an open-source repository that will contain the majority of the code, and managing the project and integration of the code with other team members (both at CMU and externally). The programmer will interface with other (external) teams to develop machine learning systems for anomaly detection in electrical utility communication networks, and will also develop internal tools for testing and benchmarking learning-based stochastic optimization algorithms.
Bachelor's or higher in computer science or closely related field
At least 1-3 years experience in software development (either as a software engineering or in similar positions in academia with a substantial software development component)
Strong proficiency in Python
Experience with Linux/OSX development environments
Familiarity with git source control
Familiarity with machine learning methodologies and libraries
Experience with project management tools such as Jira
Experience with a deep learning library (such as TensorFlow, Torch, or Theano)
Experience with scientific Python libraries (such as numpy, scipy, pandas)
Basic knowledge of optimization, such as linear/quadratic programming, and numerical methods
Experience with additional programming languages including C/C++ and CUDA
GitHub page highlighting past programming projects
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