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Ml Platform Engineer Jobs in Virginia (NOW HIRING)

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

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 ...

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

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 ...

Axiologic Solutions LLC

Platform Engineer

Springfield, VA · On-site

$162/hr

We are currently seeking a Platform Engineer Expert to support our growing team. Position Overview ... ML Model Development, ML Operations and Applied Data Science; Testing. Responsibilities: Assists ...

Tech Tammina

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer Location: Reston VA Required Qualifications • 3+ years of hands-on ... ML lifecycle tools such as MLflow, or similar. • Experience supporting GPU-based workloads or ...

Tech Tammina LLC

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer Location: Reston VA Required Qualifications · 3+ years of hands-on ... ML lifecycle tools such as MLflow, or similar. · Experience supporting GPU-based workloads or ...

540

Senior AI/ML Engineer

Arlington, VA

$120K - $165K/yr

Design model-serving platforms supporting secure, scalable, and reliable batch or real-time inference * Establish model monitoring, performance tracking, drift detection, explainability, and ...

Doist

Senior AI/ML Engineer

Arlington, VA · On-site

$140 - $210/hr

Design model-serving platforms supporting secure, scalable, and reliable batch or real-time inference * Establish model monitoring, performance tracking, drift detection, explainability, and ...

540

Senior AI/ML Engineer

Arlington, VA · On-site

$120K - $165K/yr

Design model-serving platforms supporting secure, scalable, and reliable batch or real-time inference * Establish model monitoring, performance tracking, drift detection, explainability, and ...

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All JobsMl Platform Engineer JobsMl Platform Engineer Jobs in Virginia

Ml Platform Engineer information

See Virginia salary details

$32

$63

$94

How much do ml platform engineer jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for ml platform engineer in Virginia is $63.41, according to ZipRecruiter salary data. Most workers in this role earn between $50.05 and $73.17 per hour, depending on experience, location, and employer.

What is an ML Platform Engineer?

ML Platform Engineers are specialized software engineers who design, build, and maintain the infrastructure and tools needed to support the development, deployment, and scaling of machine learning models. They bridge the gap between data science and production engineering by automating model training, monitoring, versioning, and serving. Their work enables data scientists to focus on modeling while ensuring that ML solutions are reliable, reproducible, and scalable in real-world environments.

What is the difference between Ml Platform Engineer vs Data Scientist?

AspectML Platform EngineerData Scientist
Required credentialsBachelor's/Master's in CS, Engineering, or related; experience with cloud platformsBachelor's/Master's in Statistics, Math, or CS; strong programming skills
Work environmentBuilds and maintains ML infrastructure, collaborates with engineering teamsAnalyzes data, develops models, and interprets results
Industry usageTech companies, AI startups, enterprises deploying ML systemsResearch institutions, tech firms, data-driven organizations

ML Platform Engineers focus on developing and maintaining the infrastructure that supports machine learning models, while Data Scientists primarily analyze data and build models. Both roles often collaborate but serve different functions within the AI and data ecosystem.

How does an ML Platform Engineer typically collaborate with data scientists and software engineers within a company?

ML Platform Engineers work closely with both data scientists and software engineers to streamline the process of developing, deploying, and maintaining machine learning models. They provide the infrastructure and tools necessary for data scientists to build and experiment with models efficiently, while ensuring seamless integration with production systems managed by software engineers. Regular communication, participation in cross-functional meetings, and shared project management tools are common ways teams collaborate. This close collaboration helps to bridge the gap between research and production, ensuring robust, scalable, and reliable ML solutions.

What skills and qualifications are needed to thrive as an ML Platform Engineer?

To thrive as an ML Platform Engineer, you need a strong background in computer science, software engineering, and machine learning concepts, often supported by a degree in a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), containerization (Docker, Kubernetes), CI/CD pipelines, and knowledge of ML frameworks (TensorFlow, PyTorch) are commonly required. Collaboration, problem-solving, and strong communication skills help you work efficiently with data scientists, engineers, and stakeholders. These skills ensure the development, scalability, and reliability of robust ML infrastructure that empowers teams to deploy and manage models effectively.
What are popular job titles related to Ml Platform Engineer jobs in Virginia? For Ml Platform Engineer jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Ml Platform Engineer jobs in Virginia look for? The top searched job categories for Ml Platform Engineer jobs in Virginia are:
What cities in Virginia are hiring for Ml Platform Engineer jobs? Cities in Virginia with the most Ml Platform Engineer job openings:
Ml Platform Engineer jobs near you
Infographic showing various Ml Platform Engineer job openings in Virginia as of August 2026, with employment types broken down into 52% Full Time, 44% Part Time, and 4% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $131,885 per year, or $63.4 per hour.

AI/ML Platform Engineer

Surge InfoTech LLC

Alexandria, VA • On-site

Full-time

Re-posted 10 days ago

Job description

  • /No C2C option/

    • We are seeking a hands-on Senior AI/ML Platform Engineer with 10+ years of IT experience and a strong track record of building, deploying, and operationalizing AI/ML systems. The ideal candidate is a doer who excels in implementing scalable, production-grade AI/ML solutions across cloud environments.

    Core Requirements
  • 10+ years of IT/engineering experience
  • 3+ years of handson AI/ML development experience
  • 4+ years working directly with AWS services (Lambda, EC2, S3, DynamoDB, IoT Core, API Gateway, Fargate/ECS)
  • Proven experience deploying ML systems into production environments
  • Strong coding skills and ability to build systems endtoend
    • Key Skills
  • Deep Learning frameworks: TensorFlow, PyTorch, Keras
  • LLMs, prompt engineering, NLP pipelines
  • Python and Java as primary languages; strong engineering fundamentals
  • FastAPI and microservices for ML inference
  • InfrastructureasCode (Terraform)
  • Kubernetes and Docker for scalable ML workloads
  • Distributed/cloud systems design with AWS
  • Edgetocloud system integration experience
  • Handson build experience (not just design/architecture)
    • Responsibilities
  • Build and deploy productiongrade ML/AI pipelines and services
  • Develop LLMpowered and NLPdriven applications
  • Write, optimize, and maintain highquality Pythonbased ML code
  • Implement scalable infrastructure using Terraform, AWS, and Kubernetes
  • Build FastAPIbased inference services and cloud APIs
  • Collaborate with crossfunctional engineering teams to deliver highimpact systems
  • Troubleshoot, optimize, and own systems endtoend as a handson engineer
    • Preferred Skills
  • Experience with distributed systems and microservices
  • Strong understanding of ML model lifecycle, deployment patterns, and operational monitoring

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