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Ml Platform Engineer Jobs in Washington (NOW HIRING)

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 ...

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 ...

Advise on the design and evaluation of AI/ML platform environments across cloud, hybrid, and ... Coordinate with program managers, engineers, mission stakeholders, cybersecurity teams, and ...

AI Platform Engineer

Hanover, MD · On-site

$90K - $211K/yr

Advise on the design and evaluation of AI/ML platform environments across cloud, hybrid, and ... Coordinate with program managers, engineers, mission stakeholders, cybersecurity teams, and ...

AI Platform Engineer

Hanover, MD · On-site

$90K - $211K/yr

Advise on the design and evaluation of AI/ML platform environments across cloud, hybrid, and ... Coordinate with program managers, engineers, mission stakeholders, cybersecurity teams, and ...

AI Platform Engineer - Senior

Columbia, MD · On-site

$101K - $139K/yr

... of AI/ML platform environments across cloud, hybrid, and enterprise deployments. • Support ... engineers, mission stakeholders, cybersecurity teams, and external partners to keep platform ...

AI Platform Engineer - Senior

Columbia, MD · On-site

$101K - $139K/yr

... of AI/ML platform environments across cloud, hybrid, and enterprise deployments. • Support ... engineers, mission stakeholders, cybersecurity teams, and external partners to keep platform ...

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Ml Platform Engineer information

See Washington salary details

$37

$72

$107

How much do ml platform engineer jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for ml 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 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 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.

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 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.

What job categories do people searching Ml Platform Engineer jobs in Washington look for?

The top searched job categories for Ml Platform Engineer jobs in Washington are:

What cities in Washington are hiring for Ml Platform Engineer jobs?

Cities in Washington with the most Ml Platform Engineer job openings:

Infographic showing various Ml Platform Engineer job openings in Washington as of August 2026, with employment types broken down into 54% Full Time, 37% Part Time, 2% Temporary, and 7% Contract. Highlights an 74% Physical, 2% Hybrid, and 24% Remote job distribution, with an average salary of $150,665 per year, or $72.4 per hour.

AI/ML Platform Engineer

Surge InfoTech LLC

Alexandria, VA • On-site

Full-time

Re-posted 22 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