Applied Research Mathematician / Machine Learning Security and Generative AI - Experienced to Expert Level (Maryland)

Full-time - Senior

Fort George G. Meade Complex, MD

National Security and International Affairs

About the position

NSA's Mathematics Research Group conducts world-class mathematical research with the objective of developing new and innovative techniques and technologies to support our Signals Intelligence and Cybersecurity missions, as well as the broader Intelligence Community (IC). We are actively seeking mathematicians with experience in machine learning security and generative AI to join our Statistics and Machine Learning Research Office. The office focuses on fundamental mathematics research in the applications of machine learning and statistical analysis, as well as a wide range of other technical areas to include cryptography, machine learning security, generative AI, network defense, and graph algorithms. Statistics and Machine Learning Research is interested in understanding the threats facing operationally deployed machine learning models. The ideal candidate will not only have extensive experience in developing and deploying machine learning models, but also in understanding the threats to those models and how to secure against those threats. Additionally, the candidate should be well-versed in the mathematical and statistical underpinnings of generative AI.

Responsibilities

• Analyze problems and determine procedures required to solve technical problems
• Create computer algorithms, data models, and protocols
• Identify new applications of known techniques
• Analyze data, algorithms, and communication protocols using mathematical/statistical methods
• Develop and apply mathematical or computational methods and lines of reasoning
• Design, develop and debug software solutions
• Create and maintain documentation on research processes, analyses and/or the results
• Write logical and accurate technical reports to communicate ideas
• Effectively instruct, mentor, and support the professional development of colleagues in the areas of technical expertise

Requirements

• Degree must be in Mathematics, Physics, Engineering, Data Science, Computer Science, Statistics, or a related STEM field
• Degree must include at least 24 semester credit hours (or 36 credit hours from universities on a quarter system) in advanced mathematics courses
• Relevant experience must be in one or more of the following: the design, development, use, and evaluation of mathematics models, methods, or techniques (for example, algorithm development) to study issues and solve problems
• Experience may also include network engineering, computer science, physics, software engineering, electrical engineering
• Entry is with a Bachelor's degree plus 6 years of relevant experience, or a Master's degree plus 4 years of relevant experience, or a Doctoral degree plus 2 years of relevant experience for Senior level
• Entry is with a Bachelor's degree plus 9 years of relevant experience, or a Master's degree plus 7 years of relevant experience, or a Doctoral degree plus 5 years of experience for Expert level

Nice-to-haves

• Strong technical background in a computational science discipline (e.g., Mathematics, Statistics, Data or Computer Science)
• Research experience in mathematical analysis of large data sets
• Experience applying machine learning methods, including but not limited to a subset of deep learning, reinforcement learning, ensemble methods, and large scale graph analytics
• Significant programming experience, especially working with large data sets (e.g., Python, Tensorflow, R, Java, C/C++, and/or other data processing frameworks)

Benefits

• Relocation assistance
• Flexible work schedules
• Generous leave programs
• Paid personal fitness time
• Training and continuing education classes
• Health and life insurance
• Federal Thrift Savings Plan (TSP)
• Federal retirement plan