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Machine Learning Testing Jobs in Marietta, GA (NOW HIRING)

Crew Career Center

Data Scientist

Atlanta, GA · On-site

Complete familiarity with empirical approaches to estimate performance of machine learning models including: hold-out sets, cross-validation, leave-one-out testing * Understanding of orders of ...

Patientco

Data Scientist

Atlanta, GA · On-site

Complete familiarity with empirical approaches to estimate performance of machine learning models including: hold-out sets, cross-validation, leave-one-out testing * Understanding of orders of ...

FIS

Data Scientist

Atlanta, GA · On-site

Following the machine learning lifecycle, the data scientist should be able to convert the results ... Design and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate ...

Worldpay, Inc.

Data Scientist Lead

Atlanta, GA · On-site

Following the machine learning lifecycle, the data scientist should be able to convert the results ... Design and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate ...

FIS

Data Scientist Lead

Atlanta, GA · On-site

Following the machine learning lifecycle, the data scientist should be able to convert the results ... Design and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate ...

Worldpay, Inc.

Data Scientist

Atlanta, GA · On-site

Following the machine learning lifecycle, the data scientist should be able to convert the results ... Design and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate ...

TriNet

Staff MLOps Engineer

Atlanta, GA · On-site

... machine learning model deployment, including orchestration, version control, and monitoring systems. Design and implement automation pipelines for model training, testing, validation, and deployment ...

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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 Marietta, GA is $21.63, according to ZipRecruiter salary data. Most workers in this role earn between $18.70 and $24.13 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 popular job titles related to Machine Learning Testing jobs in Marietta, GA?

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

What job categories do people searching Machine Learning Testing jobs in Marietta, GA look for?

The top searched job categories for Machine Learning Testing jobs in Marietta, GA are:

Machine Learning Testing jobs near you
Infographic showing various Machine Learning Testing job openings in Marietta, GA as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 75% Full Time, 21% Part Time, and 2% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $44,995 per year, or $21.6 per hour.

DevOps Engineer with AI Ops Experience

Atlanta, GA • On-site

Syntricate Technologies
IT Services • 51 - 200 employees
Learn more about Syntricate Technologiesexternal

$50.75 - $69.50/hr

Full-time

Re-posted 29 days ago

Key responsibilities

  • Design, implement, and manage CI/CD pipelines and ML Ops frameworks to automate AI/ML workflows.

  • Support infrastructure for machine learning models, ensuring scalability, performance, security, and cost efficiency.

  • Automate testing and deployment processes for data and model pipelines to enable reliable and fast releases.

Job description

Job Summary:
Syntricate Technologies is seeking a DevOps Engineer with AI experience. The role involves designing, implementing, and managing CI/CD pipelines and ML Ops frameworks to automate AI/ML workflows, along with supporting infrastructure for machine learning models.
Responsibilities:
• 5+ years of experience in DevOps engineering, with at least 3 years specializing in AI Ops or supporting ML/AI model deployment and infrastructure.
• Proven experience in designing, implementing, and managing CI/CD pipelines and ML Ops frameworks to automate AI/ML workflows.
• Ability to design and manage scalable infrastructure to support machine learning workloads, ensuring cost efficiency, performance, and security.
• Proficiency in automating testing and deployment processes for data and model pipelines to support fast, reliable releases.
• Familiarity with serverless architectures and cloud-native tools for AI, allowing for flexible and efficient resource management.
• Experience with security best practices, including role-based access control, data encryption, and compliance requirements for data-sensitive applications.
• Excellent communication skills with the ability to collaborate closely with data scientists, ML engineers, and software development teams.
• Proven ability to document infrastructure, CI/CD pipelines, and MLOps processes, ensuring transparency and knowledge sharing across teams.
• Strong problem-solving skills and a proactive approach to troubleshooting, particularly in managing and resolving deployment and performance issues.
• Ability to train and mentor team members on MLOps tools, best practices, and model deployment techniques.
• Experience with data security and governance standards, especially related to machine learning applications in regulated industries.
• Familiarity with AI ethics and compliance, including model fairness, transparency, and risk management.
• Knowledge of advanced monitoring and alerting tools and techniques to ensure the reliability of AI systems in production.
Qualifications:
Required:
• 5+ years of experience in DevOps engineering, with at least 3 years specializing in AI Ops or supporting ML/AI model deployment and infrastructure.
• Proven experience in designing, implementing, and managing CI/CD pipelines and ML Ops frameworks to automate AI/ML workflows.
• Proficiency in cloud platforms (AWS, GCP, Azure) with hands-on experience in deploying AI/ML models and utilizing AI/ML services (e.g., AWS SageMaker, Google AI Platform).
• Strong skills in containerization and orchestration tools such as Docker and Kubernetes, especially for deploying machine learning models at scale.
• Experience with infrastructure-as-code tools like Terraform, CloudFormation, or Ansible to manage and provision cloud and on-premise environments.
• Proficiency in CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) to build automated pipelines for AI/ML model training, testing, and deployment.
• Solid understanding of monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack) for model performance tracking and infrastructure observability.
• Strong programming and scripting skills in Python, Bash, and YAML for automating workflows and integrating services.
• Experience with MLOps best practices, including model versioning, automated retraining, and model governance for reliable and reproducible AI pipelines.
• Hands-on experience with model monitoring tools (e.g., MLflow, Kubeflow, or TFX) to track model performance, drift, and retraining needs.
• Familiarity with data pipelines and orchestration tools (e.g., Apache Airflow, Prefect) for managing data and model workflows.
• Knowledge of model deployment strategies (e.g., blue-green deployments, canary releases) to ensure reliable AI/ML model deployment with minimal downtime.
• Experience with A/B testing and experiment tracking to evaluate model performance in production and measure the impact on business KPIs.
• Ability to design and manage scalable infrastructure to support machine learning workloads, ensuring cost efficiency, performance, and security.
• Proficiency in automating testing and deployment processes for data and model pipelines to support fast, reliable releases.
• Familiarity with serverless architectures and cloud-native tools for AI, allowing for flexible and efficient resource management.
• Experience with security best practices, including role-based access control, data encryption, and compliance requirements for data-sensitive applications.
• Excellent communication skills with the ability to collaborate closely with data scientists, ML engineers, and software development teams.
• Proven ability to document infrastructure, CI/CD pipelines, and MLOps processes, ensuring transparency and knowledge sharing across teams.
• Strong problem-solving skills and a proactive approach to troubleshooting, particularly in managing and resolving deployment and performance issues.
• Ability to train and mentor team members on MLOps tools, best practices, and model deployment techniques.
Preferred:
• Experience with data security and governance standards, especially related to machine learning applications in regulated industries.
• Familiarity with AI ethics and compliance, including model fairness, transparency, and risk management.
• Knowledge of advanced monitoring and alerting tools and techniques to ensure the reliability of AI systems in production.
Company:
Syntricate Technologies offers quality assurance, validation, regulatory, business analysis, and project management services. Founded in 2004, the company is headquartered in Boston, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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