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Junior Full Stack Machine Learning Engineer Jobs in Ashburn, VA

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... This role spans the full ML lifecycle, from dataset development and experimentation to model ...

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

R0245170 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

R0242766 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Machine Learning Engineer

Ashburn, VA · On-site

$110 - $170/hr

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA. The ideal candidate will bring hands‑on experience in machine learning, advanced analytics, and ...

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

R0245828 Machine Learning Engineer The Opportunity: As an experience d AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

R0242757 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Showing results 21-40

Junior Full Stack Machine Learning Engineer information

See Ashburn, VA salary details

$47.6K

$96.7K

$145.2K

How much do junior full stack machine learning engineer jobs pay per year?

As of Aug 15, 2026, the average yearly pay for junior full stack machine learning engineer in Ashburn, VA is $96,679.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,600.00 and $97,700.00 per year, depending on experience, location, and employer.

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

AspectJunior Full Stack Machine Learning EngineerJunior Data Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops end-to-end ML applications, works on both backend and frontendAnalyzes data, builds models, and visualizes insights, mainly in data analysis tools
Employer & Industry UsageTech companies, startups, AI-focused firmsResearch institutions, tech companies, finance, healthcare

While both roles involve working with data and machine learning, the Junior Full Stack Machine Learning Engineer focuses on building complete applications with ML components, including frontend and backend development. The Junior Data Scientist primarily analyzes data, creates models, and provides insights without necessarily developing full applications.

What are popular job titles related to Junior Full Stack Machine Learning Engineer jobs in Ashburn, VA?

For Junior Full Stack Machine Learning Engineer jobs in Ashburn, VA, the most frequently searched job titles are:

What job categories do people searching Junior Full Stack Machine Learning Engineer jobs in Ashburn, VA look for?

The top searched job categories for Junior Full Stack Machine Learning Engineer jobs in Ashburn, VA are:

Infographic showing various Junior Full Stack Machine Learning Engineer job openings in Ashburn, VA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $96,679 per year, or $46.5 per hour.

Machine Learning Engineer

AI Squared

Washington, DC • On-site

Full-time

Re-posted 25 days ago


Job description

Machine Learning Engineer
Washington, DC (Hybrid)
About the Role:
We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You'll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.
Key Responsibilities:
  • Design, implement, and maintain ML deployment pipelines for scalable production systems.
  • Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
  • Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
  • Partner with data scientists to transition models from research/prototype into production-ready deployments.
  • Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
  • Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
  • Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
  • Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.

Qualifications:
  • 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
  • Proven experience deploying and maintaining machine learning models in production at scale.
  • Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
  • Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
  • Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
  • Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
  • Strong understanding of MLOps best practices, monitoring, and automation.
  • Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
  • Strong communication and collaboration skills across technical and non-technical teams.