Research Engineering/Scientist Associate IV

This position will contribute to research, development, and support activities involving big data analysis, statistical analysis, and machine learning technologies and the interfaces by which users can transparently harness these techniques and technologies for the Scalable Computational Intelligence (SCI) group at TACC.

Responsibilities

• Explore and understand new techniques and technologies related to support high performance scalable data analysis and machine learning. Responsible for assignments that require the technical knowledge to modify or adapt routine procedures under the direction of a supervisor, to meet special research requirements for data projects hosted at TACC.
• Working with data providers, consumers, systems experts, and staff to design, develop, deploy, and support scalable high-performance machine learning applications and systems at TACC. Assists fellow research associates, research scientists, engineers, or faculty members with specific phases of research projects and proposal preparation.
• Prepare user documentation and guides to support the usage and operations of data analysis tools and APIs supported and/or developed at TACC. Train and teach the capabilities and potential usages of machine learning techniques in a wide array of research domains and levels of computational abilities.
• Other related functions as assigned by manager and leadership team.

Required Qualifications

• Master’s degree in in Data Science, Computer Science, Statistics, or other related research field with a strong background in machine learning and data analytics.
• Proficient in at least two of following programming languages: Python, Java and C++
  Knowledgeable in data analysis software tools and environment, such as R, TensorFlow, Scikit-learn, pyTorch, and Spark.
• Working knowledge with website design, implementation and operation and familiar with HTML5, Java Script, and web programming framework,
• The ability to learn, adapt, and teach new technologies to enable new capabilities or improve on existing ones.
• Experience teaching data mining and machine learning to a wide array of students, researchers, and developers.
• Excellent written and verbal communications skills.

Relevant education and experience may be substituted as appropriate.

Preferred Qualifications

• Three or more years’ experience in developing and implementing machine learning algorithm/applications in an academic research environment.
• Experience implementing and supporting a machine learning and data analytics applications, workflows.
• Practical experiences with teaching, deploying and using at least one common deep learning frameworks such as Caffe, Tensorflow, Keras, PyTorch.
• Experience developing applications in large parallel environments and tools such as Hadoop, Hbase, HIVE, Elastic Search,and SparkML.
• Familiar with standard parallel computing tools and interface, such as MPI, CUDA.
• Excellent problem solving and strategic thinking skills.
• Recent peer-reviewed publications in computer science fields relevant to machine learning, big data analysis and/or high-performance computing.