

Machine Learning Engineer Principal
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Principal Machine Learning Engineer in Chantilly, VA, with a contract length of over 6 months and a pay rate of "unknown." Requires Top Secret clearance, expertise in Python and ML frameworks, and 9 years of relevant experience.
π - Country
United States
π± - Currency
$ USD
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π° - Day rate
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ποΈ - Date discovered
July 25, 2025
π - Project duration
More than 6 months
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ποΈ - Location type
Hybrid
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π - Contract type
Unknown
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π - Security clearance
Yes
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π - Location detailed
Chantilly, VA
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π§ - Skills detailed
#Computer Science #ML (Machine Learning) #Base #PyTorch #Libraries #Cloud #AI (Artificial Intelligence) #Mathematics #TensorFlow #Statistics #Keras #Python #Documentation #Programming
Role description
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Job ID: 2507773
Location: CHANTILLY, VA, US
Date Posted: 2025-07-23
Category: Engineering and Sciences
Subcategory: Machine Learning Engineer
Schedule: Full-time
Shift: Day Job
Travel: Yes, 75 % of the Time
Minimum Clearance Required: Top Secret
Clearance Level Must Be Able to Obtain: TS/SCI with Poly
Potential for Remote Work: Yes
Description
SAIC is a premier technology integrator solving our nation's most complex modernization and readiness challenges. Our robust portfolio of offerings across the defense, space, civilian, and intelligence markets includes high-end solutions in engineering, IT, and mission solutions. Using our expertise and understanding of existing and emerging technologies, we integrate the best components from our own portfolio and our partner ecosystem to deliver innovative, effective, and efficient solutions.
Position Overview
SAIC invites applications from exceptional candidates for the position of Machine Learning Engineer in our AI Innovation Factory. This is a hybrid job located in the Northern Virginia area. Up to 75% annual travel is required.
Responsibilities
β’ Assist senior team members to seamlessly integrate machine learning innovations into new and existing user-facing systems.
β’ Contribute to the development of proofs of concept, delivering value to both internal and external customers.
β’ Collaborate with your team to explore emerging ML research and technologies.
β’ Learn from industry trends, advances, and tool sets in generative AI and MLOps to expand your knowledge and fuel innovation.
β’ Assist in the technical assessment of emerging commercial and open-source solutions to ensure their applicability.
β’ Contribute to our organization's knowledge base by creating documentation and training materials focused on advanced AI techniques.
β’ Collaborate with team members to establish best practices for generative AI development.
Qualifications
Required Qualifications:
β’ A Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering or a related field with 9 years of experience. In lieu of a degree, four additional years of professional experience will be considered.
β’ Experience with Python programming language and popular machine learning libraries and frameworks such as PyTorch, Keras, TensorFlow, or scikit-learn.
β’ Excellent communication skills to collaborate effectively with cross-functional teams and non-technical stakeholders.
β’ Ability to learn and adapt quickly in a fast-paced, dynamic environment.
β’ Familiarity with AI, Cloud, Data or Software technologies and concepts is a large plus, but not mandatory.
β’ Knowledge of federal government, military experience, contracting processes, and regulations is advantageous.
SAIC is a premier technology integrator providing full life cycle services and solutions in the technical, engineering, intelligence, and enterprise information technology markets. SAIC is Redefining Ingenuity through its deep customer and domain knowledge to enable the delivery of systems engineering and integration offerings for large, complex projects. SAIC's approximately 15,000 employees are driven by integrity and mission focus to serve customers in the U.S. federal government. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $4.5 billion. For more information, visit saic.com. For information on the benefits SAIC offers, see .