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Machine Language Jobs in Virginia (NOW HIRING)

Dark Wolf

Machine Learning Engineer

Herndon, VA · On-site

Preferred : • Experience with specific areas of machine learning such as Natural Language Processing (NLP), Computer Vision, or Recommender Systems. • Experience with MLOps practices and tools ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $160K/yr

... natural language processing (NLP), vision-language models (VLMs), and other generative AI ... Machine learning experience using visual data * Understanding of a variety of machine learning ...

Dark Wolf

Machine Learning Engineer

Chantilly, VA · On-site

Preferred : • Experience with specific areas of machine learning such as Natural Language Processing (NLP), Computer Vision, or Recommender Systems. • Experience with MLOps practices and tools ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

... natural language processing (NLP), vision-language models (VLMs), and other generative AI ... Machine learning experience using visual data * Understanding of a variety of machine learning ...

Kitware

Machine Learning Engineer

Arlington, VA · On-site

$110K - $160K/yr

... natural language processing (NLP), vision-language models (VLMs), and other generative AI ... Machine learning experience using visual data * Understanding of a variety of machine learning ...

Elder Research

Machine Learning Engineer

Richmond, VA · On-site

Machine Learning Engineer Elder Research Inc., a wholly owned subsidiary of MANTECH international ... Language Processing (NLP) tasks such as entity extraction, summarization, and semantic search.

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How much do machine language jobs pay per hour?

As of Jun 5, 2026, the average hourly pay for machine language in Virginia is $40.59, according to ZipRecruiter salary data. Most workers in this role earn between $17.53 and $56.81 per hour, depending on experience, location, and employer.

What is the difference between Machine Language vs Data Scientist?

AspectMachine LanguageData Scientist
Required CredentialsNone specific; basic programming knowledgeBachelor's or higher in CS, statistics, or related fields
Work EnvironmentLow-level programming, embedded systems, hardware interactionData analysis, modeling, visualization in offices or labs
Industry UsageSoftware development, embedded systems, hardware programmingTech, finance, healthcare, marketing

Machine Language involves low-level programming directly with binary instructions, primarily used for hardware interaction. Data Scientists analyze data to extract insights, often using high-level languages like Python or R. While both roles require programming skills, Machine Language focuses on hardware-level coding, whereas Data Scientists work with data analysis and modeling.

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 a strong background in mathematics, statistics, programming (especially Python), and a relevant degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), cloud platforms, and data processing tools, as well as certifications in data science or machine learning, are highly valuable. Problem-solving skills, curiosity, and effective collaboration are crucial soft skills for innovating and working in multidisciplinary teams. These abilities are essential to design, implement, and optimize models that drive data-driven decision-making and business success.

What are some common challenges faced by professionals working with machine learning models, and how can they be addressed?

Professionals in machine learning often encounter challenges such as managing large and complex datasets, addressing data quality issues, and preventing model overfitting. Collaborating effectively with data engineers and domain experts can help ensure data relevance and accuracy. Additionally, staying updated with the latest research and best practices is important for optimizing model performance and adapting to evolving business needs. Regular code reviews and model validation are key practices that help maintain high-quality, reliable machine learning solutions.

What is machine language and what does a machine language programmer do?

Machine language is the lowest-level programming language, consisting of binary code that computers can directly execute. A machine language programmer writes instructions in this binary code to control computer hardware at the most fundamental level. This job often involves optimizing code for performance and working closely with computer architecture. Machine language programming is rare today, as higher-level languages are more commonly used, but it remains crucial for certain applications like embedded systems and hardware drivers.
What are popular job titles related to Machine Language jobs in Virginia? For Machine Language jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Machine Language jobs in Virginia look for? The top searched job categories for Machine Language jobs in Virginia are:
Machine Language jobs near you
Infographic showing various Machine Language job openings in Virginia as of May 2026, with employment types broken down into 78% Full Time, 11% Part Time, 8% Contract, and 3% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $84,418 per year, or $40.6 per hour.

Machine Learning Engineer

Dark Wolf

Herndon, VA • On-site

Full-time

Posted 21 days ago

Job description

Job Summary:
Dark Wolf constructs and deploys data management and analytics solutions for the defense and intelligence communities. They are seeking a highly motivated Machine Learning Engineer to design, develop, and implement machine learning models and algorithms to solve specific business problems.
Responsibilities:
• Design, develop, and implement machine learning models and algorithms to solve specific business problems.
• Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
• Transform machine learning models into deployable APIs and integrate them with existing applications and infrastructure.
• Collaborate closely with data scientists, software engineers, and product managers to understand requirements and translate them into practical ML solutions.
• Experiment with different machine learning techniques and algorithms to identify the most effective approaches for given problems.
• Evaluate model performance using appropriate metrics and iterate on models to improve accuracy, efficiency, and scalability.
• Monitor and maintain deployed models, ensuring their reliability and performance in production environments.
• Troubleshoot and resolve issues related to machine learning models and pipelines.
• Stay up-to-date with the latest advancements in machine learning, deep learning, and related fields.
• Contribute to the development of best practices and standards for machine learning development and deployment within the team.
• Document machine learning models, experiments, and deployment processes.
• Potentially work with large datasets and big data technologies.
• Optimize machine learning models for performance and efficiency.
Qualifications:
Required:
• Master’s in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry experience in Machine Learning, Computer Science, Data Science or related fields.
• Demonstrated hands-on experience in developing and deploying machine learning models in a production environment.
• Strong programming skills in Python and experience with relevant machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, scikit-learn, etc.
• Solid understanding of machine learning algorithms (e.g., regression, classification, clustering, dimensionality reduction, deep learning architectures).
• Experience with data preprocessing, feature engineering, and data visualization techniques.
• Familiarity with data storage and processing technologies (e.g., SQL, NoSQL databases, Spark, Hadoop).
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and their machine learning services.
• Understanding of software development principles, version control (e.g., Git), and CI/CD pipelines.
• Strong analytical and problem-solving skills with the ability to interpret data and draw meaningful conclusions.
• Excellent communication and collaboration skills to effectively communicate technical concepts to both technical and non-technical audiences.
Preferred:
• Experience with specific areas of machine learning such as Natural Language Processing (NLP), Computer Vision, or Recommender Systems.
• Experience with MLOps practices and tools for automating and monitoring machine learning workflows.
• Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes.
• Experience with building and deploying RESTful APIs.
• Familiarity with big data technologies and distributed computing.
• Experience with statistical modeling and inference.
Company:
Dark Wolf provides DevSecOps agile software development, information operations, penetration testing and incident response, applied research and rapid prototyping, machine learning, and mission support and engineering services to the Intelligence Community, national security, and Fortune 500 customers. Founded in 2009, the company is headquartered in Herndon, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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