Polaris Alpha was formed in 2016 through the merger of EOIR Technologies, Intelligent Software Solutions (ISS), PROTEUS Technologies and Intelesys. The Company has approximately 1,200 employees with major offices in Colorado Springs, CO, Fredericksburg, VA, Annapolis Junction, MD, Aberdeen Proving Ground, MD, and Alexandria, VA as well as customer sites both domestically and internationally. We’re passionate about developing cutting-edge, creative solutions, and fostering a highly sought-out place of employment for many of the brightest minds in the industry. We are the best because we hire the best.
We are a one-of-a-kind mid-tier government contractor provides leading-edge capabilities to three emerging warfighter domains critical to the future of national security: space, cyber, and the electromagnetic spectrum. At Polaris Alpha, we have developed a culture of going above and beyond the normal expectations in the delivery of our work. Our clients and employees are the number one reason why we’re successful, and that formula won’t be changing!
EOIR Technologies, a Polaris Alpha company, is seeking experienced Data Scientists/Deep Learning Engineer to support on-going research and development (R&D) efforts into the development of video and cyber analytics. This position is an opportunity to get in on the ground floor of a growing R&D development team within Polaris Alpha and work with cutting edge technologies in pursuit of novel applications of Deep Learning to meet both commercial and military needs. You will work alongside fellow researchers with a focus on transitioning general Deep Learning research into mature products with real-world applicability. Polaris Alpha has openings for both an experienced lead and as well as supporting developers, so applicants of all experience levels with Deep Learning expertise are encouraged to apply.
The successful candidate, working with a team of experts, will support the analysis, research, development, test and evaluation of Deep Learning solutions. They will be familiar with the use of popular Deep Learning frameworks and should understand the challenges and mitigation/optimization techniques for large scale data ingest, manipulation of training data, model tuning, network design and refinement, and result validation.