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Machine Learning Engineer Jobs in Hampton, VA (NOW HIRING)

CDIT LLC

AI Engineer

Norfolk, VA · On-site

The AI Engineer will design, develop, and deploy machine learning, natural language processing, and generative AI solutions supporting the NMMES program at Naval Sea Systems Command (NAVSEA) in ...

Altagrove LLC

Data Science

Norfolk, VA · On-site

Research emerging AI, machine learning, and data engineering technologies and recommend innovative applications for customer missions. * Support technical documentation, architecture development ...

Booz Allen Hamilton

Palantir Data Engineer

Norfolk, VA

$61K - $141K/yr

Ever-expanding technology like IoT, machine learning, and artificial intelligence means that there's more structured and unstructured data available today than ever before. As a data engineer, you ...

Booz Allen Hamilton

Palantir Data Engineer

Norfolk, VA · On-site

$61K - $141K/yr

Ever-expanding technology like IoT, machine learning, and artifi cia l intelligence means that there's more structured and unstructured data available today than ever before. As a data engineer, you ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Hampton, VA

Machine Learning Engineer information

See Hampton, VA salary details

$30.4K

$124.4K

$187K

How much do machine learning engineer jobs pay per year?

As of Aug 15, 2026, the average yearly pay for machine learning engineer in Hampton, VA is $124,447.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,100.00 and $149,800.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs

What are the most commonly searched types of Machine Learning Engineer jobs in Hampton, VA?

The most popular types of Machine Learning Engineer jobs in Hampton, VA are:

What job categories do people searching Machine Learning Engineer jobs in Hampton, VA look for?

The top searched job categories for Machine Learning Engineer jobs in Hampton, VA are:

What cities near Hampton, VA are hiring for Machine Learning Engineer jobs?

Cities near Hampton, VA with the most Machine Learning Engineer job openings:

Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Hampton, VA as of August 2026, with employment types broken down into 78% Full Time, and 22% Contract. Highlights an 100% In-person job distribution, with an average salary of $124,447 per year, or $59.8 per hour.

AI Engineer

CDIT LLC

Norfolk, VA • On-site

Full-time

Posted 19 days ago

Job description


The AI Engineer will design, develop, and deploy machine learning, natural language processing, and generative AI solutions supporting the NMMES program at Naval Sea Systems Command (NAVSEA) in Norfolk, VA. The role focuses on turning large volumes of ship maintenance, logistics, and readiness data into predictive insights and decision-support tools that improve fleet availability, reduce unplanned maintenance, and accelerate work-package planning.
The engineer will work directly with data scientists, software engineers, Navy subject-matter experts, and CACI program leadership to move models from prototype to production within an AWS GovCloud environment. This position requires a blend of hands-on ML engineering, MLOps discipline, and comfort operating in a Defense customer environment governed by DoD security and accreditation processes.
Key Responsibilities
• Design and build supervised, unsupervised, and generative AI models (including LLM-based RAG pipelines) against Navy maintenance, supply, and equipment-history datasets.
• Develop end-to-end ML pipelines - data ingestion, feature engineering, training, evaluation, deployment, and monitoring - using Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face).
• Implement MLOps practices in AWS GovCloud using SageMaker, Bedrock, Step Functions, Lambda, and containerized workloads (ECS/EKS).
• Apply NLP techniques (entity extraction, classification, summarization, semantic search) to unstructured maintenance narratives, casualty reports (CASREPs), and 3M records.
• Collaborate with data engineers to define schemas, feature stores, and vector databases (OpenSearch, pgvector) that support production inference.
• Establish model governance practices: version control for models and datasets, bias and drift monitoring, evaluation harnesses, and human-in-the-loop feedback loops.
• Document model design, assumptions, and limitations in a manner suitable for Government review, accreditation, and technical exchange meetings.
• Support proposal, demonstration, and pilot activities as directed by CACI and CDIT Solutions leadership.
Required Qualifications
• 5+ years of hands-on experience building and deploying ML or AI systems in production.
• Expert-level Python, including data-science tooling (pandas, NumPy, scikit-learn) and at least one deep-learning framework (PyTorch or TensorFlow).
• Demonstrated experience with LLMs, prompt engineering, retrieval-augmented generation (RAG), embeddings, and vector search.
• Working knowledge of AWS ML services - SageMaker, Bedrock, Lambda, S3, and IAM - preferably in GovCloud (US).
• Experience deploying containerized workloads (Docker, ECS, or EKS) and building CI/CD pipelines for ML.
• Solid grounding in statistics, model evaluation, and experimentation methodology.
• Ability to communicate technical concepts clearly to non-technical Navy and program stakeholders.
• Active DoD Secret clearance at time of hire.
Preferred Qualifications
• Prior experience supporting Navy, NAVSEA, or other DoD maintenance / logistics programs.
• Familiarity with Navy data sources such as NMMES-TR, Maintenance Figure of Merit (MFOM), OARS, or 3M/MDS.
• Experience with responsible-AI frameworks, model cards, and DoD AI ethics principles.
• Exposure to knowledge graphs, ontologies, or graph-based retrieval.
• TS/SCI clearance.
Education
Bachelor's degree in Computer Science, Data Science, Applied Mathematics, Statistics, or a related technical discipline. Master's or PhD strongly preferred. Additional relevant experience may be substituted for degree requirements consistent with contract labor-category definitions.
Certifications
Required
• DoD 8570 / 8140 IAT Level II baseline certification (e.g., Security+ CE) - required within 6 months of hire if not currently held.
Preferred
• AWS Certified Machine Learning - Specialty
• AWS Certified Solutions Architect - Associate or Professional
• Certified Ethical Hacker (CEH) or CISSP

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