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Machine Learning Engineer Security Clearance

Job in Washington, District of Columbia, 20001, USA
Listing for: Kruger Recruiting
Full Time position
Listed on 2026-01-23
Job specializations:
  • Engineering
    AI Engineer, Data Engineer
Job Description & How to Apply Below
Position: Machine Learning Engineer with Security Clearance
We are hiring a ML Engineer. This role is pivotal in bridging the worlds of machine learning, data engineering, and software development to enhance our AI data applications. Career advancement opportunities are available for those interested in senior engineering positions and technical leadership. As an ML Engineer, you will:
Spearhead platform upgrades, ensuring our products are at the forefront of innovation and effectiveness.
Craft and manage dynamic dashboards using the Virtualitics AI Platform Python SDK, transforming data into intuitive visuals for decision-making.
Optimize data access patterns, enhancing the efficiency and performance of our AI solutions.
Tackle runtime performance issues, ensuring high responsiveness and stability of applications.
Architect robust, scalable, and user-friendly applications, considering current trends and future growth.
Collaborate closely with Technical Product Managers to drive usability enhancements, ensuring our products meet and exceed user expectations. 🚫 What you won't do:
You won't be siloed in just one area - this role requires versatility across ML, data engineering, and software development
You won't be doing pure research without implementation Who we need:
We need a technically versatile engineer who excels at the intersection of ML and software engineering. Our ideal candidate has production ML deployment experience, strong Python skills with data libraries, and experience with big data technologies. They should be comfortable tackling performance optimization challenges and have exceptional problem-solving capabilities.

Experience with government/defense applications is highly valued. What we do not need:
Pure researchers without practical implementation experience
Engineers with only theoretical ML knowledge
Developers who have never deployed to production environments
Those who prefer working in siloed environments rather than cross-functional teams
Candidates looking for pure research positions without implementation responsibilities
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