SCI w Poly

Peraton•Laurel, MD

32d•$176,000 - $282,000•Onsite

About The Position

Peraton is seeking an AI/ML Engineer with an active TS/SCI with Poly clearance to design, develop, test, and operationalize algorithms that solve complex business challenges and mission requirements. The role involves building models to learn from data and generate predictions aligned with customer needs. The engineer will demonstrate proficiency in model architecture, data pipeline integration, and performance metrics interpretation. A strong understanding of application development, infrastructure management, data engineering, and data governance is essential. Responsibilities include training, retraining, deploying, monitoring, and improving models based on feedback to deliver scalable, high-performance solutions. This position may involve leading geographically distributed teams and serving as a primary technical point of contact, requiring strong analytical, problem-solving, and communication skills.

Requirements

Minimum of 5 years of experience in applied machine learning on programs/contracts of similar scope, type, and complexity.
Master’s or Ph.D. degree in Advanced Mathematics, Artificial Intelligence, Data Science, Computer Science, or Deep Learning from an accredited college or university.
An active TS/SCI with Poly is required.
Experience working with various ML libraries, packages, and frameworks.
Experience with standard machine learning frameworks such as: PyTorch or TensorFlow.
Design or select appropriate data and knowledge representation methods.
Recognize and apply software architecture, data modeling, and data structure principles.
Transform data science prototypes into scalable production solutions.
Provide system integration oversight.

Nice To Haves

A relevant Bachelor’s degree plus 5 additional years of machine learning experience may be substituted for a Master’s degree.
Expert knowledge of multiple programming languages, e.g. Python, Java, C, R, is desired.

Responsibilities

Designs, develops, tests, and operationalizes algorithms to solve complex business challenges and mission requirements.
Builds models to learn from data and generate predictions aligned with customer-defined business logic and operational needs.
Demonstrates proficiency across model architecture, data pipeline integration, and the application, interpretation, and presentation of performance metrics.
Maintains a strong understanding of foundational concepts in application development, infrastructure management, data engineering, and data governance.
Responsible for training, retraining, deploying, scheduling, monitoring, and continuously improving models based on iterative user and system feedback to deliver scalable, high-performance solutions.
May lead geographically distributed teams and frequently serve as the primary point of contact for related technical matters.
Select appropriate datasets for machine learning applications.
Perform statistical analysis and data interpretation.
Run and evaluate machine learning algorithms.
Use analytical results to improve and optimize models.
Train and retrain systems as required.
Research and implement a broad range of AI/ML algorithms and tools.
Verify data integrity and model output quality.
Identify data distribution differences that may impact model performance.
Run standard testing and evaluation protocols.
Oversee testing and evaluation of AI/ML algorithms through iterative design processes.
Ensure solutions meet verification and validation requirements.