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Embedded Machine Learning Engineer Jobs in North Carolina

ExtendMyTeam

Machine Learning Engineer

Cary, NC

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 ...

CoVar

Machine Learning Engineer

Durham, NC

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 ...

Vadum, Inc.

Machine Learning Engineer

Raleigh, NC · On-site

$96K - $137K/yr

We are seeking a talented and innovative Machine Learning Engineer to join our dynamic team. In this role, you will be responsible for designing and developing machine learning prototypes, as well as ...

Vadum Inc

Machine Learning Engineer

Raleigh, NC · On-site

We are seeking a talented and innovative Machine Learning Engineer to join our dynamic team. In this role, you will be responsible for designing and developing machine learning prototypes, as well as ...

Vadum Inc

Machine Learning Engineer

Raleigh, NC

We are seeking a talented and innovative Machine Learning Engineer to join our dynamic team. In this role, you will be responsible for designing and developing machine learning prototypes, as well as ...

Q2

Machine Learning Engineer

Cary, NC

As a Machine Learning Engineer, you will help build and operate production systems that power our fraud products. You'll work closely with data scientists and engineers to bring models into ...

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 fraud products. You'll work closely with data scientists and engineers to bring models into ...

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 ...

NSS

Machine Learning Engineer

Concord, NC · Remote

As an ML software developer, you will be responsible for feature development to deliver AI and machine learning solutions into our product. Your software development expertise and experience with ML ...

NSS

Machine Learning Engineer

Concord, NC · Remote

As an ML software developer, you will be responsible for feature development to deliver AI and machine learning solutions into our product. Your software development expertise and experience with ML ...

Pantheon Data LLC

Machine Learning Engineer

Charlotte, NC · On-site

$110K - $140K/yr

Position Overview Pantheon Data is seeking a Machine Learning Engineer to join our core product team. This role focuses on the development and maintenance of Intelligent Document Processing (IDP) and ...

Corning

AI/Machine Learning Engineer

Wilmington, NC · On-site

$82.05K - $112.82K/yr

Strong understanding of machine learning algorithms, data analysis, and statistical modeling techniques. * Demonstrated programming proficiency in Python and .NET/C# Education and Experience: Desired

Corning

AI/Machine Learning Engineer

Wilmington, NC

$82.05K - $112.82K/yr

Strong understanding of machine learning algorithms, data analysis, and statistical modeling techniques. * Demonstrated programming proficiency in Python and .NET/C# Education and Experience: Desired

Apple

AIML - Staff Machine Learning Engineer

Cary, NC

$212K - $386.30K/yr

... engineering, machine learning, privacy preserving system design, and more. You will be a hands on ... or embedded development experience Minimum Qualifications 8 years of professional software ...

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Embedded Machine Learning Engineer information

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

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in North Carolina? For Embedded Machine Learning Engineer jobs in North Carolina, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in North Carolina look for? The top searched job categories for Embedded Machine Learning Engineer jobs in North Carolina are:
What cities in North Carolina are hiring for Embedded Machine Learning Engineer jobs? Cities in North Carolina with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you

Machine Learning Engineer

ExtendMyTeam

Cary, NC

Full-time

Posted 12 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