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Machine Learning Platform Engineer Jobs in Washington

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps Platform Engineer to design, build, and support enterprise-grade machine learning operations ...

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps Platform Engineer to design, build, and support enterprise-grade machine learning operations ...

Full Scope

Machine Learning Engineer

Reston, VA

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly ... Familiarity with cloud platforms like AWS, Google Cloud, or Azure for model deployment and scaling.

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled ... Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed ...

AI Squared

Machine Learning Engineer

Washington, DC

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled ... Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed ...

AI Squared

Machine Learning Engineer

Washington, DC

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled ... Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed ...

Steampunk

Machine Learning Engineer

Mclean, VA · On-site +1

$115K - $150K/yr

We are looking for a more than just a "Machine Learning Engineer", but a technologist with ... Knowledge of cloud platforms such as AWS, Google Cloud, or Azure. * Experience with version control ...

Steampunk

Machine Learning Engineer

Mclean, VA

$115K - $150K/yr

We are looking for seasoned Machine Learning Engineer to work with our existing team of Data ... Knowledge of cloud platforms such as AWS, Google Cloud, or Azure. * Experience with version control ...

Tech Tammina LLC

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer Location: Reston VA Required Qualifications · 3+ years of hands-on ... machine learning workflows, including training, inference, and model monitoring. · Experience with ...

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Machine Learning Platform Engineer information

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How much do machine learning platform engineer jobs pay per hour?

As of May 29, 2026, the average hourly pay for machine learning platform engineer in Washington is $72.44, according to ZipRecruiter salary data. Most workers in this role earn between $57.16 and $83.61 per hour, depending on experience, location, and employer.

What is a Machine Learning Platform Engineer job?

A Machine Learning Platform Engineer designs, builds, and maintains the infrastructure that enables machine learning development and deployment at scale. They work on areas like data pipelines, model training workflows, monitoring, and cloud or on-premises platforms to ensure ML models run efficiently in production. Their role bridges software engineering and machine learning, focusing on automation, scalability, and reliability to support data scientists and ML engineers in delivering models faster and more effectively.

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

A Machine Learning Platform Engineer should have strong programming skills (especially in Python or Java), knowledge of machine learning frameworks (like TensorFlow or PyTorch), and experience with cloud platforms and scalable infrastructure. Familiarity with containerization tools (such as Docker and Kubernetes), CI/CD systems, and relevant certifications in cloud or machine learning technologies is highly valued. Effective problem-solving, teamwork, and clear communication are crucial soft skills for collaborating across data science and engineering teams. These capabilities enable seamless creation and maintenance of robust, high-performance machine learning platforms for scalable model development and deployment.

What does a typical day look like for a Machine Learning Platform Engineer?

A typical day for a Machine Learning Platform Engineer involves designing, building, and maintaining the infrastructure that supports data science and machine learning workflows. You might spend your time developing new features for the platform, optimizing data pipelines, deploying models, and troubleshooting technical issues alongside data scientists and engineers. Collaboration is key—you’ll often work closely with cross-functional teams to understand requirements, ensure scalability, and improve the overall machine learning lifecycle. This role offers a challenging mix of software engineering and system design, so adaptability and a proactive mindset are important for success.
More about Machine Learning Platform Engineer jobs
What are popular job titles related to Machine Learning Platform Engineer jobs in Washington? For Machine Learning Platform Engineer jobs in Washington, the most frequently searched job titles are:
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Machine Learning Platform Engineer jobs near you
Infographic showing various Machine Learning Platform Engineer job openings in Washington as of May 2026, with employment types broken down into 83% Full Time, 10% Part Time, 1% Temporary, and 6% Contract. Highlights an 96% Physical, 2% Hybrid, and 2% Remote job distribution, with an average salary of $150,665 per year, or $72.4 per hour.

MLOps Platform Engineer

Interon IT Solutions

Reston, VA • On-site

Contractor

Posted 27 days ago

Job description

#W2 only
 
Job title: MLOps Platform Engineer 
Location: Reston VA - In person interviews so need Local In EAST coast only​
Description: 
MLOps Platform Engineer 
The Data Modeling Analytics & AI Engineering team is seeking an experienced MLOps 
Platform Engineer to design, build, and support enterprise-grade machine learning operations 
capabilities. This role will play a key part in enabling scalable, reliable, and secure ML model 
development and deployment across our cloud and container platforms. 
This is a hands-on engineering role requiring strong expertise in AWS, Kubernetes (EKS), 
CI/CD automation, containerization, and ML platform operations. The ideal candidate will have 
solid engineering fundamentals combined with practical knowledge of ML workflows, 
deployment patterns, and platform reliability. 
Key Responsibilities 
Platform Engineering & Operations  
· Engineer, manage, and support MLOps platform components across AWS and EKS-based 
environments. 
· Oversee deployment, configuration, and operation of infrastructure used for ML training, batch 
inference, and real-time model serving. 
· Ensure platform availability, resilience, and performance across dev, test, and production 
environments. 
· Implement role-based access controls (RBAC), network policies, and scalable namespace 
designs within EKS. 
Model Deployment & CI/CD Automation 
· Build and support CI/CD pipelines (GitLab) for model packaging, container image builds, 
vulnerability scanning, and automated deployment flows. 
· Enable standardized model release processes including environment promotion, versioning, and 
rollback workflows. 
· Integrate CI/CD with ML frameworks, model repositories, artifacts, and runtime environments. 
Container & Kubernetes Workloads 
· Design and manage EKS workloads supporting containerized ML jobs and microservices. 
· Implement auto-scaling, resource quotas, cluster optimization, and multi-tenant workload 
isolation. 
· Support GPU and CPU-based training/inference workloads. 
Monitoring, Observability & Optimization 
· Implement logging, monitoring, and alerting for ML pipelines, model endpoints, batch jobs, 
and platform components. 
· Analyze compute, storage, and data transfer usage to optimize cost efficiency across ML 
workloads. 
· Perform incident response, root cause analysis, and long-term remediation planning. 
Collaboration & Enablement 
· Partner with Data Scientists, ML Engineers, and application teams to operationalize end-to-end 
machine learning solutions. 
· Provide technical guidance on best practices for ML model lifecycle management, deployment 
patterns, and scalable architectures. 
· Contribute to documentation, runbooks, onboarding materials, and internal knowledge bases. 
--- 
Required Qualifications 
· 3+ years of hands-on experience with AWS services, including EKS, EC2, S3, IAM, 
CloudWatch, and ECR. 
· Strong experience operating and troubleshooting Kubernetes (preferably AWS EKS). 
· Proficiency in containerization (Docker) and orchestration concepts. 
· Strong programming/scripting experience in Python and Bash. 
· Experience building and managing CI/CD pipelines (GitLab or equivalent). 
· Familiarity with machine learning workflows, including training, inference, and model 
monitoring. 
· Experience with infrastructure-as-code (Terraform or CloudFormation). 
· Experience supporting production platforms, including incident management and root cause 
analysis.