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Entry Level Machine Learning Engineer Jobs in Cary, NC

ExtendMyTeam

Machine Learning Engineer

Cary, NC · On-site

As a Machine Learning Engineer, you will help build and operate production systems that power fraud detection and risk-related products. You'll work closely with data scientists and engineers to ...

CoVar

Machine Learning Engineer

Durham, NC · On-site

The Machine Learning Engineer will develop software and machine learning algorithms to address real-world customer issues and will have opportunities to present their work to high-level customers.

CoVar

Machine Learning Engineer

Durham, NC · On-site

Machine Learning Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC, that uses artificial intelligence to solve problems that matter. We develop AI/ML tools to help the ...

CoVar

Machine Learning Engineer

Durham, NC · On-site

... machine learning, Bayesian models, etc. • B.S., preferably M.S. or Ph.D in engineering, math, computer science, or related field • Excellent technical communication skills • Ability to work in ...

RIT Solutions, Inc.

Sr Machine Learning Engineer

Raleigh, NC · On-site

$101K - $139K/yr

RIT Solutions, Inc. is seeking a Senior Machine Learning Engineer to work on production machine learning at scale. The role involves deploying LLM/GenAI/RAG systems and requires expertise in cloud ...

RIT Solutions, Inc.

Sr Machine Learning Engineer

Raleigh, NC · On-site

$101K - $139K/yr

Required 10+ yrs production ML at scale LLM/GenAI/RAG deployment Cloud (AWS/Azure/GCP) Kubernetes/containerization Agentic systems & tool orchestration MCP servers Strong Python Preferred MLOps/CI/CD ...

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Entry Level Machine Learning Engineer information

See Cary, NC salary details

$28.1K

$64.9K

$110.4K

How much do entry level machine learning engineer jobs pay per year?

As of Jun 24, 2026, the average yearly pay for entry level machine learning engineer in Cary, NC is $64,893.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,200.00 and $73,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Entry Level Machine Learning Engineer position, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

What are some typical projects or tasks an Entry Level Machine Learning Engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.

What is an Entry Level Machine Learning Engineer job?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What are the most commonly searched types of Machine Learning Engineer jobs in Cary, NC? The most popular types of Machine Learning Engineer jobs in Cary, NC are:
What are popular job titles related to Entry Level Machine Learning Engineer jobs in Cary, NC? For Entry Level Machine Learning Engineer jobs in Cary, NC, the most frequently searched job titles are:
What job categories do people searching Entry Level Machine Learning Engineer jobs in Cary, NC look for? The top searched job categories for Entry Level Machine Learning Engineer jobs in Cary, NC are:
What cities near Cary, NC are hiring for Entry Level Machine Learning Engineer jobs? Cities near Cary, NC with the most Entry Level Machine Learning Engineer job openings:
Entry Level Machine Learning Engineer jobs near you
Infographic showing various Entry Level Machine Learning Engineer job openings in Cary, NC as of June 2026, with employment types broken down into 14% Internship, 70% Full Time, 8% Part Time, and 8% Temporary. Highlights an 92% In-person, and 8% Remote job distribution, with an average salary of $64,893 per year, or $31.2 per hour.

Machine Learning Engineer

ExtendMyTeam

Cary, NC • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

The Risk & Fraud team helps customers take a proactive stance against fraud while managing the risks inherent to their business. We build and enhance products that evolve with the ever-changing fraud landscape, delivering tangible value to customers. Our solutions allow financial institutions to focus more of their time and energy on serving their customers and communities.

As a Machine Learning Engineer, you will help build and operate production systems that power fraud detection and risk-related products. You’ll work closely with data scientists and engineers to bring models into production, ensuring they are reliable, scalable, and maintainable.

You’ll gain hands-on experience working across model development, evaluation, deployment, monitoring, and ongoing improvements. This is an applied engineering role — the software you build will solve real-world problems and must be production-ready, reliable, and testable.

A Typical Day

Your Key Responsibilities

  • Build and maintain systems and pipelines that support training, evaluation, and inference for machine learning models

  • Contribute to deploying machine learning models into production environments and ensuring they run reliably at scale

  • Write clean, maintainable, and well-tested code following production engineering best practices

  • Support monitoring and troubleshooting production ML systems, including data pipelines and model performance

  • Collaborate with data scientists and engineers to productionalize models and integrate them into scalable applications

  • Help improve the reliability, scalability, and performance of ML systems over time

  • Contribute to improving tooling and infrastructure that supports the ML development lifecycle

You Are More Likely to Excel If You:

  • Enjoy autonomy in your work and take ownership of team goals while balancing speed with long-term impact

  • Have empathy for end users and measure success through both customer value and technical quality

  • Are enthusiastic about machine learning, engineering excellence, and continuous professional development

Bring Your Passion, Do What You Love. Here’s What We’re Looking For

Must-Haves

  • Bachelor’s degree in a relevant field with 2+ years of related experience, or equivalent practical experience

  • Proficiency in Python

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

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

Nice to Have

  • Experience building end-to-end ML systems, including data pipelines, model training, deployment, and monitoring

  • Experience deploying or integrating machine learning models into applications

  • Experience building APIs, backend services, or working with distributed systems

  • Familiarity with cloud platforms such as AWS, GCP, or Azure

  • Exposure to MLOps concepts such as CI/CD and model monitoring

  • Experience working with large datasets or data processing frameworks

  • Experience with additional programming languages such as TypeScript

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