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Internship Aws Machine Learning Jobs in Raleigh, NC

ExtendMyTeam

Machine Learning Engineer

Cary, NC

Exposure to cloud platforms such as AWS, GCP, or Azure * Understanding of taking machine learning models from research/development into production systems Additional Information * Hybrid work ...

Q2 Software, Inc.

Machine Learning Engineer

Cary, NC · On-site

As a Machine Learning Engineer, you will help build and operate production systems that power our ... Familiarity with cloud platforms (AWS, GCP, or Azure). * Exposure to MLOps concepts such as CI/CD ...

Q2

Machine Learning Engineer

Cary, NC

As a Machine Learning Engineer, you will help build and operate production systems that power our ... Familiarity with cloud platforms (AWS, GCP, or Azure). * Exposure to MLOps concepts such as CI/CD ...

OptiTrack

Machine Learning & Operations Engineer

Durham, NC · Remote

$71.10K - $96.20K/yr

About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale ... Experience with cloud platforms (AWS, GCP, or Azure) * Strong understanding of automation ...

OptiTrack

Machine Learning & Operations Engineer

Durham, NC · Remote

$67.20K - $90.80K/yr

About the Role OptiTrack is seeking a Machine Learning Engineer to help design, automate, and scale ... Experience with cloud platforms (AWS, GCP, or Azure) * Strong understanding of automation ...

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

As of May 30, 2026, the average yearly pay for internship aws machine learning in Raleigh, NC is $41,395.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,600.00 and $44,700.00 per year, depending on experience, location, and employer.
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Machine Learning Engineer

ExtendMyTeam

Cary, NC

Full-time

Posted 10 days ago

Job description

Join a high-growth financial technology organization focused on building modern digital banking, payments, lending, and risk solutions for financial institutions and fintech partners. This team is investing in machine learning and analytics capabilities to help improve fraud detection, predictive insights, and operational decision-making across customer-facing products.

This is an opportunity to work on applied machine learning systems that directly support real-world fraud and risk workflows. The team owns solutions end-to-end and is focused on building scalable, production-ready ML applications that deliver measurable customer impact.

Position Summary

We are seeking a Machine Learning Engineer to help design, deploy, and support production machine learning systems within a collaborative engineering organization. This individual will work closely with software engineers, data scientists, and product teams to operationalize machine learning models, improve ML infrastructure, and support scalable analytics workflows.

This is a hands-on engineering role focused on production systems, model deployment, APIs, pipelines, and ML operations rather than purely research-oriented machine learning work.

Responsibilities

  • Build and maintain systems and pipelines supporting machine learning training, evaluation, inference, and monitoring

  • Deploy and support machine learning models in production environments

  • Write clean, scalable, maintainable, and well-tested Python code

  • Support monitoring, troubleshooting, and optimization of production ML systems and data pipelines

  • Collaborate cross-functionally with engineering, data science, and product teams to operationalize ML solutions

  • Improve the reliability, scalability, and performance of ML infrastructure and services

  • Contribute to tooling and processes that support the machine learning development lifecycle

  • Participate in code reviews, technical discussions, and collaborative problem solving

Required Qualifications

  • 2+ years of experience in machine learning engineering, software engineering, or related technical experience

  • Strong Python development experience

  • Experience working with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn

  • Experience deploying or supporting machine learning models in production environments

  • Experience writing clean, maintainable code and using version control tools such as Git

  • Exposure to cloud platforms such as AWS, GCP, or Azure

  • Understanding of taking machine learning models from research/development into production systems

Additional Information

  • Hybrid work environment based in Cary, NC

  • Applicants must be authorized to work in the U.S. without sponsorship

  • Competitive compensation, benefits, flexible time off, and career development opportunities