ML) Engineer 1

Peraton•Laurel, MD

20h

About The Position

Designs, develops, tests, and deploys AI/ML algorithms to address complex business challenges and mission requirements. Develops models capable of learning from data, generating accurate predictions, and aligning with customer-defined business logic and operational objectives. Demonstrates expertise in model architecture, data pipeline integration, and the application, interpretation, and presentation of performance metrics. Maintains a strong understanding of application development, infrastructure management, data engineering, and data governance principles to support scalable and efficient AI/ML solutions. May lead geographically distributed teams and frequently serve as a primary point of contact for AI-related initiatives. Requires strong analytical thinking, problem-solving abilities, and exceptional written and verbal communication skills.

Requirements

Minimum of 2 years of experience in applied machine learning on programs/contracts of similar scope, type, and complexity.
Bachelor of Science degree in Advanced mathematics (e.g., calculus, linear algebra, Bayesian statistics), Computer Science or related STEM discipline from an accredited college or university. (In lieu of a bachelor’s degree, 3 additional years of machine learning experience on similar projects may be substituted).
An active TS/SCI with Poly is required.
Experience in working with various ML libraries and packages.

Nice To Haves

Expert knowledge of multiple programming languages, e.g. Python, Java, C, R, is desired.

Responsibilities

Designs, develops, tests, and deploys AI/ML algorithms to address complex business challenges and mission requirements.
Develops models capable of learning from data, generating accurate predictions, and aligning with customer-defined business logic and operational objectives.
Demonstrates expertise in model architecture, data pipeline integration, and the application, interpretation, and presentation of performance metrics.
Maintains a strong understanding of application development, infrastructure management, data engineering, and data governance principles to support scalable and efficient AI/ML solutions.
May lead geographically distributed teams and frequently serve as a primary point of contact for AI-related initiatives.
Select appropriate data sets.
Perform statistical analysis.
Run machine learning algorithms.
Use results to improve models.
Train and retrain systems when needed.
Run standard test and evaluation protocols.
Provide system integration oversight.
Experience training, retraining, deploying, scheduling, monitoring, and improving models based on iterative user and system feedback is required to support designing and creating scalable solutions for optimal performance.