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Aws Machine Learning Engineer Jobs in Virginia (NOW HIRING)

Ametek

Machine Learning Engineer

Herndon, VA · Hybrid

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 ...

Hirekeyz Inc

Sr. Machine Learning Engineer

Fort Belvoir, VA · On-site

$118K - $162K/yr

Role: Sr. Machine Learning Engineer Location: Ft. Belvoir, VA (On-site with Hybrid Option) Duration ... S. Army Command to create cybersecurity solutions working with cloud-based architecture (AWS ...

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 ...

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Booz Allen Hamilton

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

R0242757 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Booz Allen Hamilton

Machine Learning Engineer

Mclean, VA · On-site

$77K - $176K/yr

R0241353 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Booz Allen Hamilton

Machine Learning Engineer

Mclean, VA

$77K - $176K/yr

Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct ...

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All JobsAws Machine Learning Engineer JobsAws Machine Learning Engineer Jobs in Virginia

Aws Machine Learning Engineer information

See Virginia salary details

$31.2K

$127.7K

$191.8K

How much do aws machine learning engineer jobs pay per year?

As of Jun 28, 2026, the average yearly pay for aws machine learning engineer in Virginia is $127,665.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,600.00 and $153,700.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AWS Machine Learning Engineer, and why are they important?

To thrive as an AWS Machine Learning Engineer, you need strong proficiency in machine learning algorithms, programming languages like Python, and a solid understanding of cloud architecture, typically supported by a degree in computer science or a related field. Familiarity with AWS services such as SageMaker, Lambda, and S3, as well as relevant certifications like AWS Certified Machine Learning – Specialty, is highly valuable. Strong problem-solving, collaboration, and communication skills set top performers apart in this role. These skills ensure successful design, deployment, and optimization of scalable machine learning solutions on AWS that meet business needs.

What are AWS Machine Learning Engineers?

AWS Machine Learning Engineers are specialized professionals who design, build, deploy, and manage machine learning models using Amazon Web Services (AWS) cloud infrastructure. They leverage AWS tools and services, such as SageMaker, to create scalable and efficient machine learning solutions for businesses. Their responsibilities include data preparation, model training, optimization, deployment, and monitoring in a cloud environment. AWS Machine Learning Engineers often collaborate with data scientists, software engineers, and DevOps teams to integrate machine learning models into production systems.

How does an AWS Machine Learning Engineer typically collaborate with data scientists and DevOps teams?

As an AWS Machine Learning Engineer, you’ll work closely with data scientists to operationalize models, ensuring they are scalable and production-ready on AWS platforms. You’ll also frequently collaborate with DevOps teams to automate deployment pipelines, monitor model performance, and manage infrastructure using AWS services like SageMaker, Lambda, and CloudFormation. This cross-functional teamwork is essential for maintaining reliable, efficient ML workflows and for quickly resolving issues that arise in live environments.

What is the difference between Aws Machine Learning Engineer vs Data Scientist?

AspectAws Machine Learning EngineerData Scientist
CredentialsAWS certifications, machine learning coursesStatistics, data analysis, programming skills
Work EnvironmentCloud platforms, AWS services, deployment pipelinesData analysis, modeling, research environments
Industry UsageTech, finance, healthcare using AWS for ML solutionsResearch, analytics, business intelligence
Search/Comparison IntentFocus on cloud-based ML deployment and engineeringFocus on data analysis and modeling

While both roles involve working with data and machine learning, Aws Machine Learning Engineers specialize in deploying ML models on AWS cloud platforms, focusing on infrastructure and scalable solutions. Data Scientists primarily analyze data, build models, and generate insights, often using a variety of tools and programming languages. The roles overlap in skills but differ in their primary focus and work environment.

What are popular job titles related to Aws Machine Learning Engineer jobs in Virginia? For Aws Machine Learning Engineer jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Aws Machine Learning Engineer jobs in Virginia look for? The top searched job categories for Aws Machine Learning Engineer jobs in Virginia are:
Aws Machine Learning Engineer jobs near you
Infographic showing various Aws Machine Learning Engineer job openings in Virginia as of June 2026, with employment types broken down into 58% Full Time, 35% Part Time, and 7% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $127,665 per year, or $61.4 per hour.
Machine Learning Engineer

Machine Learning Engineer

Ametek

Herndon, VA • Hybrid

Other

Posted 21 days ago

AMETEK rating

7.6

Company rating: 7.6 out of 10

Based on 44 frontline employees who took The Breakroom Quiz

64th of 139 rated electronics manufacturers

Job description

We are seeking an earlycareer Machine Learning Engineer who is excited to grow rapidly by building and deploying productiongrade ML systems. The ideal candidate has a strong engineering mindset, has contributed to shipping ML features or products endtoend, and is eager to take ownership across the full lifecycle-from data pipelines to model design to deployment, monitoring, and iteration in realworld environments.

This role offers handson exposure to applied ML, working with IoT datasets, user needs, and product requirements to build scalable solutions that deliver measurable customer ROI.

Responsibilities:

  • Design, build, and deploy ML models into production environments, ensuring reliability, scalability, and performance.
  • Ability to select and apply the appropriate ML approach for a given problem - including supervised learning (e.g., logistic regression, random forest, gradient boosting), unsupervised learning (e.g., clustering, dimensionality reduction), and deep learning techniques when appropriate.
  • Develop and maintain feature engineering pipelines, data preprocessing flows, and training workflows.
  • Collaborate with crossfunctional partners including product, data engineering, DevOps & QA to deliver endtoend ML solutions.
  • Work with DevOps team to implement robust MLOps practices, including versioning, CI/CD for ML, monitoring/alerting, automated retraining, and model governance.
  • Continuously evaluate and improve models by monitoring performance, identifying and addressing bias, detecting data or concept drift, and iterating on features, algorithms, or training processes to maintain reliability over time.
  • Ensure solutions meet security, compliance, and data privacy standards.
  • Document system architectures, modeling decisions, and operational procedures.
  • Work in a high performing scrum team to deliver quality code for stakeholders.

Qualifications - Must Have Skills:

  • 3+ years of professional experience as an ML Engineer, Applied Scientist, or Data Scientist with an emphasis on handson software engineering responsibilities, particularly around productionizing models.
  • Demonstrated contributions to shipping ML models into production-not just prototypes-and supporting their maintenance over time.
  • Proficiency in Python and ML frameworks such as PyTorch and Scikitlearn.
  • Prior hands-on experience with cloud platforms (AWS, Azure, GCP) and ML services (e.g., SageMaker, Vertex AI, Azure ML).
  • Familiarity with GenAI system components and architecture, including vector databases, LLM finetuning, embeddings pipelines, and retrievalaugmented systems (RAG).
  • Experience with MLOps tooling: Docker, Kubernetes, MLflow, Feature Stores, CI/CD pipelines is preferred.
  • Strong understanding of data structures, algorithms, software engineering fundamentals, and distributed systems concepts.
  • Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Engineering, Mathematics, or a closely related quantitative field.
  • This is a hybrid role in Herndon, VA and no relocation assistance is able to be provided.

Other Beneficial Skills:

  • Familiarity with emerging Agentic AI concepts.
  • Familiarity with Edge ML patterns.
  • Experience working with large-scale data pipelines using Spark, Flink, Beam, or similar frameworks.
  • Experience or demonstrated interest in Vision ML, with familiarity in common vision models and techniques for image classification, object detection, and segmentation.
  • Knowledge of observability and monitoring tools for ML systems (Prometheus, Grafana, etc.)
  • Experience with cloud infrastructure and managing resources in the cloud.
  • Master's degree in a relevant field may be considered equivalent to up to 2 years of professional ML engineering experience, particularly when supported by handson coursework, research, internships, or realworld projects involving applied machine learning.

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