Carnegie Mellon University, Pittsburgh Supercomputing Center
September 11, 2018
Full Time - Experienced
The Mellon College of Science (MCS) is home to four departments and many programs and research centers that cross disciplines. We approach scientific problems from fresh angles using creative interdisciplinary approaches while drawing on our departmental strengths in the core sciences.
Our Pittsburgh Supercomputing Center (PSC) within MCS is a national leader in converged artificial intelligence (AI), big data, and high-performance computing (HPC). PSC is a joint project of Carnegie Mellon University (CMU) and the University of Pittsburgh (Pitt), operating in CMU’s Mellon College of Science. PSC architects and operates powerful computational and data resources and engages with academic and private-sector researchers to develop innovative solutions across the sciences, engineering, and business, using futuristic technologies that are uniquely available at PSC and leveraging strong relationships across CMU and Pitt.
PSC is seeking a Machine Learning Research Scientist. In this role, you will work within the PSC’s Artificial Intelligence and Big Data (AI&BD) Group to engage and enable research, assist with user support, as well as develop and deliver advanced training. This is an excellent opportunity if you thrive on opportunity for collaboration with complementary groups across PSC, particularly Computational Biology, User Support for Scientific Application, Facilities & Technology, and Public Health Applications.
Responsibilities are determined by active project needs, some examples are as follows:
Engage deeply in domain-specific projects with academic and private-sector researchers to develop and test prototype solutions applying high performance computing capabilities to data science challenges (HPDA).
Assist in the development of best practices for scalable AI, including elements of benchmarking and comparative evaluation involving various software frameworks and advanced hardware platforms.
Develop advanced training content for briefings, seminars, workshops, and tutorials, and assist with its delivery.
Install, test, and deploy AI and data analytics software on PSC’s production and research platforms.
Provide advanced user support for topics involving AI, data, and data analytics.
Contribute to, and possibly lead (depending on experience), grant proposals and related efforts to attract funding.
Publish results in peer-reviewed journals and conferences.
Represent PSC externally: at conferences, across CMU and Pitt, and to other stakeholders.
Flexibility, excellence, and passion are vital qualities within PSC. Inclusion, collaboration and cultural sensitivity are valued competencies at CMU. Therefore, we are in search of a team member who is able to effectively interact with a varied population of internal and external partners at a high level of integrity. We are looking for someone who shares our values and who will support the mission of the university through their work.
You will demonstrate:
Proficiency with one or more mathematical/statistical programming package: Python numpy/scipy/pandas, R, MATLAB, etc.
Proficiency with one or more deep learning frameworks and environments: TensorFlow, Caffe2, PyTorch, Anaconda, etc.
Preferred: Demonstrated success with machine learning (preferably deep learning) projects beyond coursework, i.e., in research or business.
Proficiency with Python programming and package installation.
Proficiency with Linux: shells, editors, building applications, etc.
Excellent communication skills and ability to work in a team environment.
Excellent problem-solving skills and creativity.
Ability to handle multiple priorities and a multi-disciplinary environment.
Minimum Bachelor’s Degree in Machine Learning, Data Science, or Statistics, or another field of science or engineering with relevant experience with AI frameworks and data analytics.
Preferred Master’s Degree in Machine Learning, Data Science, or Statistics, or another field of science or engineering with relevant experience with AI frameworks and data analytics.
Minimum three years of experience applying machine learning to real-world problems; education will be considered in lieu of experience.