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Machine Learning Testing Jobs in Washington (NOW HIRING)

Aqua IT

Machine Learning Engineer

Springfield, VA · On-site

$125 - $150/hr

... testing, and operational excellence * - 3+ years of machine learning/statistical modeling data analysis tools and techniques Preferred Qualifications: * - Master's degree or above in Science ...

New

aqua IT

Machine Learning Engineer

Herndon, VA

... testing, and operational excellence - 3+ years of machine learning/statistical modeling data analysis tools and techniques Preferred Qualifications: - Master's degree or above in Science, Technology ...

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Solve complex problems by writing and testing application code, developing and validating ML models ...

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Solve complex problems by writing and testing application code, developing and validating ML models ...

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 ... Solve complex problems by writing and testing application code, developing and validating ML models ...

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 ... Solve complex problems by writing and testing application code, developing and validating ML models ...

CACI

Machine Learning Engineer

Chantilly, VA · On-site

$131K - $290K/yr

Machine Learning Engineer Job Category: Information Technology Time Type: Full time Minimum ... Conduct testing and debugging of ML applications to ensure reliability and accuracy Support the ...

AeroVironment

Machine Learning Engineer III

Centreville, VA · On-site

$129K - $197K/yr

... testing. Citizenship * To qualify for this role US citizenship is required. Position Responsibilities * Support development of computer vision and machine learning algorithms capable of detection ...

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 ... Solve complex problems by writing and testing application code, developing and validating ML models ...

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Solve complex problems by writing and testing application code, developing and validating ML models ...

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 ... Solve complex problems by writing and testing application code, developing and validating ML models ...

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Machine Learning Testing information

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How much do machine learning testing jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for machine learning testing in Washington is $25.85, according to ZipRecruiter salary data. Most workers in this role earn between $22.31 and $28.85 per hour, depending on experience, location, and employer.

What is a machine learning testing?

A Machine Learning Testing job involves evaluating and validating machine learning models to ensure they function correctly, efficiently, and ethically. This includes testing for accuracy, reliability, bias, and performance under different conditions. Professionals in this role employ techniques such as unit testing, integration testing, data validation, and model performance monitoring. They also work closely with data scientists and engineers to debug issues and improve model robustness. The goal is to ensure that machine learning systems perform as expected and meet business or regulatory requirements.

What are the typical challenges faced by professionals in machine learning testing roles?

Professionals in Machine Learning Testing often encounter challenges such as dealing with non-deterministic model outputs, insufficient or imbalanced datasets, and unclear or evolving testing criteria. They may need to work closely with data scientists and engineers to develop robust test cases and validation methods tailored for dynamic machine learning systems. Staying updated on advancements in testing methodologies and tools is also important, as the field evolves rapidly. Successfully overcoming these challenges leads to higher quality models and more reliable AI solutions for end users.

What are the key skills and qualifications needed to thrive in machine learning testing, and why are they important?

To excel in Machine Learning Testing, you need a solid understanding of machine learning concepts, data analysis, and programming skills in languages like Python, as well as a background in quality assurance or software testing. Familiarity with frameworks such as TensorFlow, PyTorch, automated testing tools, and relevant certifications like ISTQB are highly beneficial. Strong attention to detail, analytical thinking, and effective communication skills help testers identify issues and collaborate with data scientists and developers. These competencies are essential to ensure the reliability, fairness, and accuracy of machine learning models deployed in production environments.

How do I become a machine learning testing?

To become a machine learning testing professional, you typically need a strong background in computer science, programming skills in languages like Python or Java, and knowledge of machine learning frameworks such as TensorFlow or PyTorch. Gaining experience with data analysis, model evaluation, and testing methodologies, along with relevant certifications or training, can improve your qualifications for this role.

What are the most commonly searched types of Machine Learning Testing jobs in Washington?

The most popular types of Machine Learning Testing jobs in Washington are:

What are popular job titles related to Machine Learning Testing jobs in Washington?

For Machine Learning Testing jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Machine Learning Testing jobs in Washington look for?

The top searched job categories for Machine Learning Testing jobs in Washington are:

Machine Learning Testing jobs near you
Infographic showing various Machine Learning Testing job openings in Washington as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 18% Part Time, 2% Temporary, and 5% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $53,762 per year, or $25.8 per hour.

Machine Learning Engineer Role

Washington, DC • On-site

OpenDataJobs
See more jobs at OpenDataJobsexternal

Full-time

Re-posted 3 days ago

Job description

The work
Machine Learning Engineers make machine learning and AI models reproducible, deployable, scalable, and supportable. They build the path from training data and experimentation to a versioned model service that can be released, monitored, retrained, and retired without guesswork.
The role centers on the model lifecycle and the platform beneath it. Machine Learning Engineers automate training and validation, manage features and model artifacts, optimize inference, implement machine learning operations (MLOps), and watch for changes in data, behavior, performance, reliability, and cost. They create the shared tooling that lets data scientists and application engineers move models into production safely.
What you'll build
• Reproducible training, validation, tuning, and retraining pipelines with versioned data, code, parameters, environments, and model artifacts.
• Model-serving systems and APIs designed for appropriate latency, throughput, availability, scaling, and rollback.
• Feature pipelines, feature stores, model registries, lineage records, approval workflows, and automated release controls.
• Monitoring and alerting for data quality, drift, model performance, fairness, infrastructure health, latency, and cost.
• Reusable libraries, templates, environments, and delivery pipelines that give data scientists a tested path from experiment to production.
Who you are
You are comfortable at the seam between modeling and software engineering. You can inspect a model, harden a pipeline, diagnose a production failure, and improve the platform so the same class of problem is easier to prevent next time.
You value repeatability over heroics. You work closely with data scientists on model behavior, data engineers on reliable inputs, AI Engineers on application integration, and platform and security teams on the environment in which the model runs.
What you bring
• Strong programming and software-engineering practice, including testing, version control, packaging, automation, and production debugging.
• Working knowledge of model development, evaluation metrics, feature engineering, data splitting, tuning, and the limits of different modeling approaches.
• Experience with training and inference pipelines, containers, cloud or on-premises compute, artifact management, and automated deployment.
• Practical MLOps experience with model registries, lineage, reproducibility, monitoring, drift analysis, retraining, release controls, and rollback.
• The ability to balance model quality with reliability, interpretability, security, privacy, latency, throughput, and cost.
About OPEN Data Jobs
OPEN Data Jobs connects AI, data, and software professionals with critical roles, primarily in the federal sector. Registering with ODJ can put your profile in view for multiple positions across several clients.
Register for Machine Learning Engineer Role
Click Apply below to register for Machine Learning Engineer Role.
Requirements
What openings may require
An opening may emphasize predictive models, computer vision, natural language models, ranking, anomaly detection, recommender systems, edge inference, generative AI model operations, or an enterprise ML platform. Some openings will focus more on model development, while others will focus more on serving and platform engineering.
Specific openings may name Python, SQL, Java, model frameworks, distributed-processing tools, cloud ML services, container orchestration, graphics processing units, feature stores, model registries, experiment tracking, or infrastructure as code. OPEN Data Jobs will identify the required depth for each opening
Benefits
Compensation, benefits, work location, and employment terms are set for each specific opening and will be stated with that opening

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