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Machine Learning Platform Engineer Jobs in Washington

Geico

Machine Learning Engineer II

Bethesda, MD

Staff Machine Learning Engineer Overview: As a Staff Machine Learning Engineer, you will be the ... Expertisein cloud platforms (AWS, Azure, GCP) and containerization technologies such as Docker, as ...

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site

$140K - $150K/yr

... Machine Learning, Cyber Security and Cutting Edge Technology across the US Government. Be a part of ... Cloud and Platform Engineering * Leverage AWS services including S3, EC2, Lambda, SageMaker, and ...

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site +1

$140K - $150K/yr

... Machine Learning, Cyber Security and Cutting Edge Technology across the US Government. Be a part of ... Cloud and Platform Engineering * Leverage AWS services including S3, EC2, Lambda, SageMaker, and ...

MORSE Corp

Machine Learning Engineer

Arlington, VA · On-site

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role ... Experience with cloud platforms (AWS and Azure) * Experience with Docker * Experience with MLOps ...

SAIC

Machine Learning Developer

Arlington, VA · Hybrid

SW Engineer Schedule: Full-Time Shift: Day Job Travel: Yes - 10% of the time Minimum Clearance ... The Machine Learning Developer will collaborate with software engineers to create innovative ML/AI ...

Ametek

Machine Learning Engineer

Herndon, VA · On-site

Prior hands-on experience with cloud platforms (AWS, Azure, GCP) and ML services (e.g., SageMaker ... Machine Learning, Artificial Intelligence, Data Science, Engineering, Mathematics, or a closely ...

SAIC

Machine Learning Developer

Arlington, VA · Hybrid

SW Engineer Schedule: Full-Time Shift: Day Job Travel: Yes - 10% of the time Minimum Clearance ... The Machine Learning Developer will collaborate with software engineers to create innovative ML/AI ...

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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 30, 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.
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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.
Machine Learning Engineer

Machine Learning Engineer

Venture Global LNG

Arlington, VA

Other

Posted 28 days ago

Job description

Venture Global LNG ("Venture Global") is a long-term, low-cost provider of American-produced liquefied natural gas. The company's two Louisiana-based export projects service the global demand for North American natural gas and support the long-term development of clean and reliable North American energy supplies. Using reliable, proven technology in an innovative plant design configuration, Venture Global's modular, mid-scale plant design will replace traditional designs as it allows for the same efficiency and operational reliability at significantly lower capital cost.

The Machine Learning Engineer will design, develop, and maintain the productionization of machine learning, deep learning, generative AI, large language models, simulation, and optimization algorithms. This includes building pipelines for training and deploying deep learning and other machine learning algorithms and enabling models to run efficiently in production. The main data engineering work will be done in Databricks and PySpark.

The ideal candidate will have excellent technical proficiency, excellent communication skills, a self-driven mindset, and the willingness to continuously learn new things.

This position will report to the Director of Business Intelligence and is structured within IT under the Vice President of Applications.

The position will be located in Arlington, VA and will require commuting to the office 5 days a week.

Responsibilities

  • Work with business stakeholders to define project requirements.
  • Orchestrate, scale, setup and improve model serving pipelines.
  • Improve model accuracy through feature engineering, tuning, and observability.
  • Improve model computational performance through all aspects of the pipeline, including tuning clusters/job compute, partitioning, caching, feature engineering code, tuning setup, etc.
  • Integrate machine learning models into production environments, ensuring reliability and scalability.
  • Evaluate pretrained models and software from vendors and support integration into production environments.
  • Develop comprehensive project plans for implementing machine learning and AI projects including solution architectures, resourcing, and dependencies.
  • Provide ETL requirements to data engineers to effectively curate files for data analytics.
  • Work with data scientists, data engineers, and business analysts to translate business requirements into machine learning solutions.
  • Build software solutions that are maintainable, scalable and provide quantifiable business value.
  • Continuously focus on quality architecture, quality code, and ruthless management of technical debt.
  • Continuously push the practice forward, learning and testing newer and better ways of performing work.

Required Qualifications

  • 5 years of machine learning engineering, software engineering, or data science experience.
  • Bachelors in a quantitative field of study.

Preferred Qualifications

  • Masters in a quantitative field of study.
  • Experience with the Azure, AWS, or other cloud ecosystems.
  • Experience in building secure data processing pipelines.
  • Proficient in utilizing data lakes, CI/CD pipelines, Databricks, Unity Catalog, and Git.
  • Experience working with streaming.
  • Expertise in building machine learning solutions using cloud data services.
  • Exceptional skills in data processing languages such as SQL, Python, or Scala.
  • Exceptional skills in feature engineering, model optimization, and parameter tuning.

Venture Global LNG is an Equal Opportunity Employer. We do not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law.

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