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Embedded Machine Learning Engineer Jobs in Folkston, GA

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

AI Solutions Engineering Delivery Lead

Jacksonville, FL · On-site

$95K - $125K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Lead Forward Deployed Engineer - AWS

Jacksonville, FL · On-site

$95K - $125K/yr

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ years of experience building reliable, maintainable, and well-documented code * Ability to travel 50 ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

NGA AI Engineer Manager

Jacksonville, FL · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Collaborate with data engineering teams to define data requirements, optimize data pipelines, and ensure availability of high-quality data for analytics and machine learning initiatives. * Ensure ...

Collaborate with data engineering teams to define data requirements, optimize data pipelines, and ensure availability of high-quality data for analytics and machine learning initiatives. * Ensure ...

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Embedded Machine Learning Engineer information

See Folkston, GA salary details

$66.3K

$145.3K

$164.8K

How much do embedded machine learning engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for embedded machine learning engineer in Folkston, GA is $145,282.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,600.00 and $163,900.00 per year, depending on experience, location, and employer.

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 are the key skills and qualifications needed to thrive as an embedded machine learning engineer?

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 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 cities near Folkston, GA are hiring for Embedded Machine Learning Engineer jobs?

Cities near Folkston, GA with the most Embedded Machine Learning Engineer job openings:

Machine Learning Operations Engineer

Mosai

Jacksonville, FL • On-site

$150 - $200/hr

Other

Posted 10 days ago


Job description

About Mosai

Mosai™ is the intelligent care coordination platform that brings together the fragmented pieces of healthcare into a clear, connected picture. Like a mosaic, our platform unites data, people, and processes so providers can make better decisions, coordinate care in real time, and deliver improved outcomes. With Mosai, home-based care organizations can thrive in value-based care while giving every patient the right care, in the right place, at the right time. Learn more at https://www.mosai.com/

Position Summary

We are seeking an experienced Machine Learning Ops (MLOps) Engineer to architect, develop, and maintain the full lifecycle of data and model pipelines that power training, inference, evaluation, and analytics workflows. This role is responsible for ensuring the reliability, scalability, and observability of all machine learning systems in production, including traditional ML models and modern LLM-based/MCP-orchestrated architectures. A key focus of this role in the near term is auditing and consolidating our existing pipelines and deployment processes. The ideal candidate is highly skilled in Python, Jupyter, Snowflake, and both Azure and AWS cloud environments, and thrives in environments requiring continuous monitoring, rapid issue diagnosis, and rigorous validation before deployment.

Job Duties
  • Design, build, and maintain scalable data pipelines supporting model training, inference, batch processing, and real-time analytics workflows.
  • Audit, refactor, and consolidate existing ML pipelines and deployment processes to eliminate technical debt, redundant workflows, and undocumented manual steps.
  • Audit, refactor, and consolidate existing ML pipelines and deployment processes to eliminate technical debt, redundant workflows, and undocumented manual steps.
  • Monitor and deploy and deploy production ML pipelines to identify anomalies, performance degradations, or failures related to data quality, logic defects, or infrastructure issues.
  • Execute rapid troubleshooting and root-cause analysis followed by timely remediation, validation, and full regression testing prior to redeployment.
  • Collaborate with Data Science, Engineering, and Product teams to operationalize machine learning models—including LLM-based and MCP-orchestrated systems—ensuring seamless integration into production environments.
  • Develop CI/CD workflows, model deployment strategies, and automated testing frameworks to support reliable, repeatable releases.
  • Implement and maintain observability tooling (logging, monitoring, alerting) to ensure high availability and traceability of ML systems.
  • Manage and optimize cloud infrastructure across Azure and AWS for compute, storage, orchestration, and security needs.
  • Create and maintain documentation, runbooks, and best practices for model operations and system maintenance.
  • Perform all other job-related duties as assigned.
Minimum Requirements
  • Bachelor’s Degree in Computer Science, Engineering or equivalent work experience.
  • 5–7 years of combined experience in Data Engineering, MLOps, Machine Learning Engineering, or related fields.
  • Demonstrated experience operationalizing traditional ML models as well as LLM-based and MCP-orchestrated systems.
  • Strong working knowledge of both Azure and AWS cloud platforms, including compute orchestration, networking, and security best practices.
  • Experience with CI/CD tools, containerization (Docker), infrastructure-as-code, and ML pipeline frameworks.
  • Strong ability to diagnose and resolve pipeline failures, data anomalies, and complex system issues.

Advanced proficiency in Python, Jupyter, and common ML/analytics frameworks.

  • Hands-on experience with Snowflake or similar cloud data warehousing environment.
  • Excellent problem-solving skills, attention to detail, and a proactive, self-directed work ethic.
  • Strong communication skills and comfort working in fast-paced, cross-functional environments.
Work Environment
  • This role is preferred to be based in Nashville or Jacksonville, near Mosai’s offices.
Physical Demands of Our Work Environment
  • This position uses a computer and other office equipment as needed to perform duties. The in-office noise level in the work environment is typical of that of an office. Frequent interruptions may be encountered throughout the workday.
  • The employee is required to either stand or sit, talk and hear frequently required to use repetitive keying or hand motions.
  • The physical demands are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Mosai is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status, If you are unable to submit an application because of a incompatible assistive technology or disability, please contact us at careers@mosai.com. We will make every effort to respond to your request for disability assistance as soon as possible.

Mosai is an E-verify employer. Your eligibility to work in the United States will be verified through the E-verify system if you apply and are selected for a position.

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