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Machine Learning Engineer

ManTechUnited States🌍 Remote
Full-timeSenior
$129k - $215k
per year
👁️ 0 views📝 0 applicationsPosted 8/3/2026Expires 9/3/2026

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Job Description

Elevate your career with MANTECH International Corporation! Join a dynamic team dedicated to national security through cutting-edge technology. Since 1968, MANTECH has led in delivering advanced solutions to government intelligence, the Department of Defense, and Federal Civilian sectors.

Dive into innovation in Digital Transformation, Cybersecurity, IT, Data Analytics and Software Development. Your journey to impactful work and rapid growth starts now—be extraordinary at MANTECH!

***This is for a future opportunity*** MANTECH seeks a motivated, career and customer-oriented Machine Learning Engineer to join our team.

The Machine Learning Engineer will leverage their strong technical background and knowledge to support highly scalable machine learning-based applications, including both pipelines and services, processing large volumes of data.

This role involves collaborating with Data Scientists to design tools and train models using data from across the enterprise to support mission and business functions.

Responsibilities

include but are not limited to: Collaborate with cross-functional teams—including data scientists, engineers, and architects—to build machine learning models, training tools, and simulation environments that support business functions.

Implement the full MLOps lifecycle to deploy, operationalize, scale, and manage automated machine learning models and analytical solutions. Develop and test ML applications according to requirements, running targeted experiments to optimize overall system performance.

Train and embed machine learning models into applications using programming languages (Python, Java, R) and core libraries (TensorFlow, Keras, Scikit-learn). Explore and visualize data to uncover key insights and identify specific factors that impact model accuracy and performance.

Manage and deploy cloud-based ML services across major cloud computing environments, including AWS, Azure, or Google Cloud Platform (GCP). Follow Agile methodologies to deli