System Engineer

Innovative Systems Lab

The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign provides supercomputing and advanced digital resources for the nation's science enterprise. At NCSA, University of Illinois faculty, staff, students, and collaborators from around the globe use advanced digital resources to address research grand challenges for the benefit of science and society. NCSA has been advancing one third of the Fortune 50® for more than 30 years by bringing industry, researchers and students together to solve grand challenges at rapid speed and scale.

NCSA is currently seeking one or more System Engineers in its Innovative Systems Lab (ISL) who will provide key technical expertise to design, develop, and run the leading-edge deep learning computational facility. The incumbent will work as part of a team providing key hardware and software design and development functions for the National Science Foundation (NSF) funded project to develop, deploy, and operate a computational system to accelerate machine learning research. The position involves evaluating, installing, upgrading, maintaining, supporting, and servicing hardware and software as well as developing hardware and software for the deep learning computing platform. This position will be responsible for the quality and optimal use of the deep learning system and the seamless integration of various systems and services enabling leading-edge computing for academic projects.

NCSA is committed to increasing the diversity of the campus community. Candidates who have experience working with a diverse range of faculty, staff, and students, and who can contribute to the climate of inclusivity are encouraged to apply.

Key responsibilities

  • Analyze hardware and software needs for the deep learning computer system, and interface with industry partners to develop and/or acquire prototype and production hardware and software that meet these needs.
  • Evaluate, install, configure, test, and update commercial hardware and software on the deep learning computer system. Evaluate, integrate, and debug, where necessary, open source software solutions for the deep learning computer system.
  • Co-develop system-level and application-level hardware and software that utilizes architectural components, such as GPUs, FPGAs, non-volatile memory, and high-speed interconnect.
  • Monitor use of system resources and adjust configurations and/or design and implement system enhancements to achieve optimal use of system resources and/or user performance.
  • Perform detailed performance measurements and analyze performance of hardware and applications.
  • Design and implement storage and security systems and redundant backups to maintain data integrity and safety.
  • Analyze and resolve system problems, including functional as well as performance issues.
  • Maintain documentation on system capabilities and equipment, both for internal group use and external users.
  • Advise application developers, users, and researchers on optimal use of the deep learning computing system.
  • Train other staff and consultants on support of new capabilities and services.

Required education and experience

  • BS degree in computer science, engineering or related field required. Alternative degree fields may be considered/accepted if accompanied by equivalent experience (depending on nature and depth of experience as it relates to current NCSA technologies).
  • Minimum two years of relevant experience as a system engineer, research programmer, or in similar capacity working with distributed systems.
  • Experience in the management and support of UNIX and/or Linux systems.
  • Programming experience with several of the following languages/systems: C, C++, python, shell programming, website development.
  • Training/experience with GPU programming utilizing CUDA and/or OpenCL.
  • Training/experience with mapping applications to FPGAs using VHDL and/or Verilog.
  • Training/coursework or practical experience in machine learning/deep learning.
  • Strong verbal and written communication skills.

Preferred experience

  • Advanced degree in computer engineering, computer science, or related field.
  • Experience with Intel FPGA SDK for OpenCL and/or Xilinx Vivado/SDAccel development environment.
  • Experience in deep neural networks (CNN, RNN), deep learning methodology (unsupervised learning, reinforcement learning, network reduction), and deep neural network optimization and acceleration.
  • Experience with developing and running applications on HPC platforms.
  • Experience with applying machine learning/deep learning techniques for solving complex scientific or engineering problems.

This is a regular academic professional position at NCSA and is an annually renewable, 12/12, 100%-time appointment with regular University benefits. Salary is commensurate with experience and start date will be as soon as possible after the close date of the search. Applicants must possess required education and experience by start date of position. Interviews and hires may occur before the closing date; however, all applications received by the closing date will receive full consideration.

To apply, please create your candidate profile at and upload your cover letter and CV/resume by the close date, December 1, 2017. Contact information for three references must be included on the application (letters may also be uploaded or sent to the contact below). For full consideration, candidates must complete the application process by the above date. The University of Illinois conducts criminal background checks on all job candidates upon acceptance of a contingent offer.

Illinois is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, sex, age, status as a protected veteran, or status as a qualified individual with a disability. Illinois welcomes individuals with diverse backgrounds, experiences, and ideas who embrace and value diversity and inclusivity. Visit

For further information regarding our application procedures, you may visit or email