Research Engineer - Optimization and Dynamical Systems

Join CACI's Cyber Science and Technology Division to apply advanced algorithms and tackle complex optimization and dynamical systems challenges. You'll research, develop, and implement innovative solutions using state-of-the-art methods-including machine learning-integrated into larger hardware and software systems. This role offers technical leadership, opportunities to present to customers, and involvement in business development.

Responsibilities
Develop and justify performance of algorithms for optimization, signal processing, and dynamical systems
Conduct detailed modeling and simulation, quantifying impacts of candidate solution architectures
Oversee development of standalone applications and major software subsystems, ensuring adherence to software best practices
Assist in creating detailed technical plans, including tasks, schedules, and labor estimates
Develop automated tests to rigorously evaluate algorithms and software capabilities
Contribute as a core team member during proposal development and project execution

Required Qualifications
Master's degree in computer science, applied mathematics, physics, or related discipline, plus at least 5 years' experience
Proven experience developing algorithms and software applications
Strong background in optimization, signal processing, dynamical systems theory, and analysis of linear/nonlinear systems
Significant experience with Python and C++
Proficiency with Linux operating systems and utilities
Experience with standard modeling and simulation software packages, tools, and methods
Ability to anticipate strengths and weaknesses of algorithmic solutions; perform simulation, testing, and evaluation to compare alternatives quantitatively
Effective communication skills for presenting research outcomes to senior staff and customers
Demonstrated leadership in organizing and executing technical efforts
ship with ability to obtain, maintain, or transfer required security clearance
Top Secret clearance

Desired Qualifications
Ph.D. in computer science, applied mathematics, physics, or related discipline
Experience integrating machine learning methods with signal processing and/or time-series data analysis
Knowledge of information theory, probability theory, and statistical tests (parametric/non-parametric)
Experience with data-driven approaches to systems analysis
Top Secret with SCI preferred