The Georgia Tech Research Institute (GTRI) is the nonprofit, applied research division of the Georgia Institute of Technology (Georgia Tech). Founded in 1934 as the Engineering Experiment Station, GTRI has grown to more than 2,900 employees, supporting eight laboratories in over 20 locations around the country and performing more than $940 million of problem-solving research annually for government and industry.
Responsibilities
- reading and understanding current research in specific areas of interest
- using conventional tools and frameworks to train models and carry out experiments with them
- working at the interface between high-level programming and low-level hardware to deploy these models onto devices that perform real-time inference with streaming sensor data
- integrating feedback from laboratory experiments
- manipulating data sets that are simulated, received from others, or generated by sensor devices
Requirements
- Experience with Python
- Familiarity with Pytorch
- Knowledge of software engineering practice including tools like git
- Familiarity with Linux and Bash
- Exposure to basic networking tools and concepts
- Familiarity with common tools and procedures in machine learning, from data collection and cleaning to training and inference
- Coursework in machine learning
Other
- Pursuing a Master's degree in Computer Science, Mathematics, or another related STEM discipline
- Full-Time Intern- Must be registered for Full-time Internship Credit
- 3.5 or higher cumulative GPA
- Atleast one year of experience using Linux in a classroom or professional setting (preferred)
- Knowledge of networking concepts (preferred)
- Expertise using git and version control SAAS, such as Gitlab or Github, as demonstrated by knowledge of pipelines, actions, and the general CI work flow (preferred)
- Experience building and deploying software in a Linux cluster environment (preferred)
- Experience with Linux cluster management software like Slurm (preferred) or PBS and associated tools like Environment Modules (preferred)
- Exposure to working directly with edge hardware, ASICs, FPGAs, or other low-level hardware (preferred)
- Exposure to digital logic (preferred)
- Significant participation in machine learning projects, whether in a classroom or professional setting, and the ability to speak clearly to the technical aspects of those projects (preferred)
- 40 hours per week
- Due to our research contracts with the US federal government, candidates for this position are required to be US Citizens