Supports research efforts by implementing technical solutions to novel research problems. Works closely with senior research staff to transform research ideas into implemented solutions. Contributes to larger team efforts in implementing research prototypes. Contributes to documentation and publications as needed. Independently develops parts of a larger system. Initiates the design, development and implementation of technology research projects. Investigates the feasibility of applying scientific principles and concepts to potential inventions and products. Plans and executes applied research.
The annual base salary range for this position is $90,000 - $105,000. When extending an offer of employment, the University of Southern California considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience, education/training, key skills, internal peer equity, federal, state and local laws, contractual stipulations, grant funding, as well as external market and organizational considerations.
Minimum Education: Master's degree
Addtional Education Requirements Combined experience/education as substitute for minimum education
Minimum Experience:
Minimum Skills: Knowledge of one or more relevant programming languages (e.g., Python, C++, Goolang). Experience with Linux. Experience with Virtual LAN and/or Virtual Extensible LAN. Experience with Melanox Switches. Experience with SDN programming. Experience with Ansible, Bash and Nagios. Experience with Prometheus. Familiarity with and Simple Network Management Protocol. Experience with applicable IT protocols (e.g., Border Gateway Protocol, Secure Shell, Transport Layer Security). Experience with iptables and Network File System. xperience with iptables and Network File System. Position requires U.S. citizen with active Top Secret (TS) clearance and SCI eligibility.
Preferred Experience: 3 years
Preferred Skills: Experience in research. Experience with artificial intelligence, very large-scale integration design, machine learning, deep learning (e.g., TensorFlow, PyTorch, HuggingFace), and Dockerization.
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