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Freelance Full Stack Machine Learning Engineer Jobs in Washington, DC

AI Squared

Machine Learning Engineer

Washington, DC · On-site

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 ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

Support the full machine learning lifecycle, taking computer vision models from experimentation and ... Strong programming skills in Python and hands-on experience with deep learning frameworks ...

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Showing results 21-40

All JobsFull Stack Machine Learning Engineer JobsFreelance Full Stack Machine Learning Engineer JobsFreelance Full Stack Machine Learning Engineer Jobs in Washington, DC

Freelance Full Stack Machine Learning Engineer information

See Washington, DC salary details

$50.4K

$152.6K

$215.8K

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

As of Sep 10, 2026, the average yearly pay for freelance full stack machine learning engineer in Washington, DC is $152,641.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,700.00 and $179,000.00 per year, depending on experience, location, and employer.

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

AspectFreelance Full Stack Machine Learning EngineerFreelance Data Scientist
CredentialsProficiency in programming, machine learning, and full stack developmentStrong statistical, analytical, and programming skills, often with data analysis certifications
Work EnvironmentDevelops and deploys ML models, works on both front-end and back-end systemsAnalyzes data, builds models, and provides insights, mainly focusing on data analysis
Industry UsageUsed in tech, finance, healthcare for deploying ML solutionsUsed across industries for data analysis, reporting, and predictive modeling

Freelance Full Stack Machine Learning Engineers focus on building and deploying machine learning models within full stack applications, combining software development with ML expertise. Freelance Data Scientists primarily analyze data and create models for insights. While both roles require programming skills, the engineer's role emphasizes deployment and integration, whereas the data scientist's role centers on analysis and interpretation.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in Washington, DC?

The most popular types of Full Stack Machine Learning Engineer jobs in Washington, DC are:

What are popular job titles related to Freelance Full Stack Machine Learning Engineer jobs in Washington, DC?

For Freelance Full Stack Machine Learning Engineer jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Freelance Full Stack Machine Learning Engineer jobs in Washington, DC look for?

The top searched job categories for Freelance Full Stack Machine Learning Engineer jobs in Washington, DC are:

Freelance Full Stack Machine Learning Engineer jobs near you
Infographic showing various Freelance Full Stack Machine Learning Engineer job openings in Washington, DC as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution, with an average salary of $152,641 per year, or $73.4 per hour.

Machine Learning Engineer

Washington, DC • On-site

AI Squared
See more jobs at AI Squaredexternal

Full-time

Re-posted 21 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.

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