Research Science / Senior AI Machine Learning Research Engineer

CACI International•Aurora, CO

29d

About The Position

CACI has an exciting new opportunity for a Senior AI and Machine Learning Research Engineer. Apply machine learning, statistics, to develop algorithms to solve challenging problems, signal processing, and computer networking domains. In this role, you'll leverage your strong foundation in machine learning, data science, and signal processing to solve complex challenges in the RF domain.

Requirements

Master’s degree in quantitative field with mathematical underpinnings and at least 4 years’ experience.
Must have active Top Secret security clearance.
Experience developing models,.
Strong background in machine learning, mathematics and statistics.
Comfortable using Linux operating systems and commonly used Linux utilities.
Must be a US Citizen with the ability to obtain, maintain and/or transfer the required security clearance as dictated by the contract

Nice To Haves

Ph.D. in computer science, computer engineering, or machine learning, Statistics, applied mathematics or Physics.
Experience applying machine learning to signal processing and/or other time-series data analysis applications.
Knowledge of or experience with information theory, probability theory, parametric and non-parametric statistical tests.
Familiarity with concepts and techniques associated with adversarial AI and AI/ML assurance.
Active Top Secret/SCI clearance preferred.

Responsibilities

Strong mathematical foundation in statistics, linear algebra, and calculus with demonstrated ability to understand and implement machine learning algorithms from first principles rather than solely relying on pre-built libraries.
Proficiency in designing and building data pipelines, including experience with ETL processes, data warehousing solutions, and optimizing workflows for large-scale data processing.
Hands-on experience with cloud-based infrastructure (e.g., AWS, Azure, GCP) for deploying ML solutions, including containerization, orchestration, and CI/CD pipelines for model deployment.
Programming expertise in Python and SQL, with experience using data engineering frameworks (e.g., Spark, Airflow) and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Demonstrated experience in establishing ML governance practices, including version control for datasets and models, experiment tracking, model monitoring, and implementing reproducible research principles.