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Machine Learning Data Engineer Jobs in Virginia (NOW HIRING)

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Machine Learning Engineer

Reston, VA

You will work closely with data scientists, engineers, and product managers to design, develop, and deploy machine learning models and solutions that drive business value. Key Responsibilities:

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$44.1K

$128.6K

$176K

How much do machine learning data engineer jobs pay per year?

As of May 30, 2026, the average yearly pay for machine learning data engineer in Virginia is $128,604.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,500.00 and $136,300.00 per year, depending on experience, location, and employer.

What is a Machine Learning Data Engineer job?

A Machine Learning Data Engineer is responsible for designing, building, and maintaining the data infrastructure that supports machine learning models. They develop data pipelines, ensure data quality, and optimize data storage for efficient processing. This role involves working with large-scale datasets, implementing ETL processes, and collaborating with data scientists to deploy machine learning models. Strong knowledge of databases, cloud platforms, and programming languages like Python and SQL is essential. Their work enables organizations to leverage machine learning effectively by providing reliable and scalable data solutions.

What are the key skills and qualifications needed to thrive in the Machine Learning Data Engineer position, and why are they important?

To thrive as a Machine Learning Data Engineer, you typically need strong programming skills in Python or Scala, a deep understanding of data structures, algorithms, and machine learning concepts, as well as a degree in computer science or a related field. Experience with big data tools like Spark, Hadoop, and cloud platforms such as AWS or Azure, along with knowledge of data pipelines and ETL processes, is highly valuable; certifications in these areas can be advantageous. Problem-solving ability, attention to detail, and strong communication skills help professionals excel when working with diverse technical teams and stakeholders. These skills ensure data engineers can effectively build reliable, scalable data systems that support the development and deployment of machine learning models.

What are the typical daily responsibilities of a Machine Learning Data Engineer?

As a Machine Learning Data Engineer, your daily responsibilities often include designing, building, and maintaining data pipelines that efficiently move and transform data for machine learning applications. You may clean, preprocess, and validate large datasets, optimize storage solutions, and work closely with data scientists to ensure data is accessible and usable for model training and evaluation. Regular collaboration with software engineers and business analysts is common to align project goals and solve data-related challenges. Staying up to date with the latest tools and technologies is also important, as you'll help enable scalable and efficient deployment of machine learning solutions.

What engineers make $300,000 a year?

Senior machine learning data engineers with extensive experience, advanced skills in data pipelines, cloud platforms, and programming languages like Python or Scala can earn $300,000 or more annually. High compensation often reflects leadership roles, specialized expertise, or work at large tech companies and financial institutions.
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Machine Learning Data Engineer jobs near you
Artificial Intelligence / Machine Learning Data Engineer

Artificial Intelligence / Machine Learning Data Engineer

MAG Aerospace

Fairfax, VA • On-site

$116.80K - $140.20K/yr

Full-time

Posted 2 days ago

Job description

Position Summary
MAG Aerospace is staffing for a Artificial Intelligence / Machine Learning Data Engineer.
This position will lead the development of intelligent systems that transform multi-modal sensor data into actionable intelligence for tactical operations. You'll leverage COTS, FOSS/OSS, and custom development to build or integrate everything from edge computer vision to conversational AI assistants, while managing the data pipelines that feed these systems in the most challenging environments. While you'll have a core expertise in either data engineering or model development, you have a passion for mastering the full stack of AI systems.
US Citizens Only
Former US Defense Contractor / US Gov / US Military Experience Only
This is a Hybrid Position - Remote mainly - but as well on call to come into a MAG office when requested
We are seeking candidates who live in proximity to our corporate HQ in Fairfax, VA primarily but will entertain persons living near our satellite offices in:
Aberdeen, MD - Titusville, FL - Newport News, VA - Carthage NC
Essential Duties and Responsibilities
Duties include, but not limited to:
Primary Responsibilities:
  • Develop and optimize data-centric AI solutions such as computer vision pipelines for object detection, tracking, and classification
  • Implement advanced AI capabilities including RAG systems, agentic workflows, and fine-tuned LLMs
  • Design and deploy edge-optimized models using TensorRT, ONNX, and quantization techniques
  • Build data engineering pipelines for ETL, feature engineering, and model training
  • Create analytics dashboards and business intelligence solutions for operational insights
  • Implement multi-modal sensor fusion algorithms (visual, thermal, acoustic, RF)
  • Design and maintain data lakes, warehouses, and real-time streaming architectures
  • Develop conversational AI interfaces using open-source LLMs (Llama, Mistral, etc.)
  • Establish and enforce data quality standards, validation checks, and governance procedures throughout the data lifecycle
  • Develop and implement robust testing and validation strategies for AI/ML models, including performance under degraded data conditions, adversarial testing, and operational scenarios
Secondary Responsibilities:
  • Optimize AI workloads for embedded platforms (Jetson, Intel Neural Compute Stick)
  • Implement hardware acceleration using CUDA and TensorRT
  • Profile and optimize memory/power consumption for edge devices
  • Support embedded systems team with AI-specific hardware integration
  • Design distributed inference systems for degraded network conditions

Requirements
Minimum Requirements:
Primary Experience / Qualifications:
  • 5+ years' experience in machine learning, AI, and data engineering
  • Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, JAX)
  • Experience with modern AI paradigms (transformers, diffusion models, neural ODEs)
  • Hands-on experience with LLM deployment and optimization (vLLM, TGI, llama.cpp)
  • Proficiency with data engineering tools (Apache Spark, Airflow, dbt, etc.)
  • Experience with both SQL and NoSQL databases at scale
  • Knowledge of vector databases and embedding systems (Pinecone, Weaviate, pgvector)
  • Experience with computer vision libraries (OpenCV, PIL) and video processing
  • Understanding of MLOps practices and model lifecycle management
Preferred Qualifications
  • Experience with military/defense AI applications
  • Knowledge of agentic AI frameworks (LangChain, AutoGPT, CrewAI)
  • Familiarity with federated learning and edge-cloud hybrid architectures
  • Experience with business intelligence tools (Tableau, PowerBI, Grafana)
  • Knowledge of time-series analysis and anomaly detection
  • Experience with knowledge graphs and semantic reasoning
  • Understanding of explainable AI and model interpretability
  • Experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, Weights & Biases)
  • Published research or patents in relevant areas

Education & Experience:
  • Bachelor's degree in CS, EE, or related field;
  • Master's preferred

Clearance:
  • Must be eligible for Secret security clearance

Other Qualifications:
  • Must be a US citizen

Special Note
What Makes You Successful Here
  • You can build anything from a computer vision pipeline to a conversational AI assistant
  • You treat data engineering as seriously as model development
  • You understand the tradeoffs between cloud-scale and edge deployment
  • You can explain complex AI concepts to operators and executives alike
  • You see AI as a tool for augmenting human decision-making, not replacing it

Why Join MAG:
  • Work on meaningful problems that directly impact national security
  • Small, elite team where your contributions matter immediately
  • Access to cutting-edge hardware and technologies
  • Rapid prototyping environment - see your ideas deployed in weeks
  • Direct interaction with end users and field deployments
  • Professional development and conference attendance support
  • Flexible work arrangements with occasional field exercises
  • Opportunity to shape the future of tactical edge computing

Company Policy
MAG Aerospace (MAG) is an Equal Opportunity/Affirmative Action Employer and is committed to Diversity and Inclusion. We encourage diverse candidates to apply to our positions.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
Click below for the "EEO is The Law" and "Pay Transparency Nondiscrimination" supplement posters.
https://www.dol.gov/agencies/ofccp/posters
MAG Aerospace (MAG) is committed to providing an online application process that is accessible to all, including individuals with a disability, by offering an alternative way to apply for job openings. This alternative method is available for those who cannot otherwise complete the online application due to a disability or need for accommodation.
MAG provides reasonable accommodation to applicants under the guidance of the Americans with Disabilities Act (ADA), Section 503 of the Rehabilitation Act of 1973, the Vietnam-Era Veterans' Readjustment Assistance Act of 1974, and certain state and/or local laws.
If you need assistance due to a disability, please contact the MAG Aerospace Recruiting email:
Applicant.Assist@magaero.com or call (703) 376-8993.

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