Machine Learning Engineer (TS/SCI)

Vantor•Herndon, VA

2d•$116,000 - $169,400•Onsite

About The Position

Vantor is hiring a Machine Learning Engineer for an R&D program focused on identifying, maturing, and operationalizing advanced AI/ML and computer vision capabilities to address mission needs. The program brings together best-of-breed industry capabilities to deliver scalable, high-performance AI/ML and Automatic Target Recognition (ATR) solutions, while accelerating technology transition, enterprise adoption, and customer mission impact. The position contributes to multidisciplinary engineering teams developing and evaluating machine learning solutions that use multi-INT data. The engineer also supports machine learning operations pipelines for continuous integration and deployment of computer vision models, develops analytics and derivative data products, and applies best practices for version control, reproducibility, and governance. The role calls for a self-motivated engineer comfortable exploring data, testing innovative approaches, and adapting when an approach does not work. The position requires U.S. citizenship and an active U.S. Government TS/SCI clearance; candidates must be willing to obtain a CI polygraph. Employment in cleared roles is contingent on clearance verification. The role is onsite. The Washington, DC metropolitan-area base pay range is $116,000–$154,000–$169,400 annually; ranges vary by location, and offers depend on experience, qualifications, skills, location, and market conditions.

Requirements

U.S. citizenship and an active TS/SCI clearance; willingness to obtain a CI polygraph.
At least 5 years of programming experience in at least one general-purpose programming language, such as Python, TypeScript, Java, C, or C++.
Bachelor’s degree in Statistics, Mathematics, Data Science, Computer Science, Engineering, Physics, or a related field.
Proficiency with the Python data science and AI/ML stack (such as PyTorch, TensorFlow, NumPy, pandas, Matplotlib, and scikit-learn) and geospatial libraries (such as GDAL, GeoPandas, and Shapely), or equivalent.
Experience extracting, manipulating, visualizing, and analyzing large datasets.
Strong understanding of machine learning principles and model lifecycle management.
Strong problem-solving mindset and adaptability, including the ability to leverage emerging technologies to address mission challenges.

Nice To Haves

Ability to work effectively in a fast-paced, dynamic environment and manage multiple priorities.
Experience designing, developing, and deploying AI-native software products that transform large-scale geospatial data into actionable intelligence and mission-critical insights.
Demonstrated software development experience, including design, development, and implementation.
Experience designing, developing, testing, and deploying diverse AI/ML or computer vision models across different data types.
Experience with containerization and orchestration technologies, including Docker and Kubernetes, in cloud or hybrid environments.
Familiarity with modern software development tools, DevSecOps practices, and CI/CD pipelines.
Proactive approach to troubleshooting and problem-solving.
Proficiency in Linux-based environments.
Proficiency in a multiparadigm programming language such as Python, Java, Go, or Rust.

Responsibilities

Contribute to multidisciplinary AI/ML engineering teams to design, implement, and evaluate machine learning solutions leveraging multi-INT data.
Contribute to the design, development, and implementation of MLOps pipelines for continuous integration and deployment of computer vision models.
Collaborate with engineers, clients, and users.
Develop analytics and derivative data products to help users extract meaningful knowledge.
Implement best practices for version control, model reproducibility, and governance.