Back to Jobs
EmployeeTechnology

Machine Learning Engineer with Security Clearance

Gray Wolf SolutionsMcLean, VA🇺🇸United StatesPosted Oct 23, 2026

Quick overview

SeniorityMid Senior
Employment typeEmployee
Work modeHybrid
LocationMcLean, VA, United States
PostedYesterday
FlaskSQLGitTableauDeep LearningJenkinsKerasLLMPyTorchPythonTensorFlowMachine Learning

Job description

GRAY WOLF SOLUTIONS MACHINE LEARNING ENGINEER McLean, VA area Active TS/SCI and FS poly required REQUIRED SKILLS, DEMONSTRATED EXPERIENCE, CERTIFICATIONS: • Professional or academic experience in testing, evaluating, or validating the performance of AI systems with a focus on LLM-based systems. (KEY EXPERIENCE FOR CUSTOMER) • Professional or academic experience in experiment design and execution to test the performance of LLMs against a benchmark. (KEY EXPERIENCE FOR CUSTOMER) • Leveraging GPUs for accelerated computing. (KEY EXPERIENCE FOR CUSTOMER) • Professional or academic experience with deep learning frameworks such as PyTorch, Tensorflow, or Keras. (KEY EXPERIENCE FOR CUSTOMER) • Tuning neural networks, such as LLMs, on custom data sets and applying results to specific use cases. • Professional or academic experience developing models and ensembles in the AI/ML space, including selecting the best Python libraries for a given task, choosing appropriate pre-processing actions, performing analysis, and assessing model performance. • Professional or academic experience using Python or R. • Professional or academic experience and proficiency with SQL to include using common table expressions, set operations, aggregated functions and nested subqueries. • Professional or academic experience with version control systems such as Github, Jenkins.

HIGHLY DESIRED SKILLS AND DEMONSTRATED EXPERIENCE • HuggingFace Transformers library and hub. • Development of benchmarks to measure LLM performance • Cloud computing development and architecture. • Front-end web development frameworks such as Flask. • Creating machine learning models that conduct text classification and topic modeling in Python using standard machine learning (SciKit Learn-) or deep learning models. • Developing applications for semantic search. • Tableau to produce visualizations and dashboards. • Communicating methodological choices and model results. • Verification and validation test benches. • Explainable AI (XAI) techniques. • Open Neural Net Exchange (ONNX)

Similar jobs