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Embedded Machine Learning Engineer Jobs in Folkston, GA

Kemper

Senior Data Scientist

Jacksonville, FL · On-site

Independently designs, develops, validates, and implements statistical and machine-learning ... Collaborates with data scientists, data engineers, and business partners to develop scalable ...

Deloitte

Cyber Manager - AI SOC

Jacksonville, FL · On-site

$102K - $139K/yr

Serving as an embedded engineering lead with client teams to translate operational workflows and ... Experience applying artificial intelligence, machine learning, or large language model workflows to ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Folkston, GA

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 10, 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:

Embedded Machine Learning Engineer jobs near you

Information Technology_USA - USA_Developer

Jacksonville, FL • On-site

Real Soft, Inc.
IT Services • 501 - 1,000 employees
Learn more about Real Soft, Inc.external

Contractor

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

Please strictly adhere to the following resume naming convention:
ALL CAPS, NO SPACES B/T UNDERSCORES
: /hr
PTN_US_GBAMSREQID_CandidateBeelineID
i.e. PTN_US_9999999_SKIPJOHNSON0413
MSP Owner: Shilpa Bajpai
Location: Tampa, FL-- 100% onsite position
Duration: 6 months
skill id: 10631330
Key Responsibilities:
• AI Development: Design and implement Generative AI applications using frameworks like LangChain, LlamaIndex, and LangGraph.
• Agentic Solutions: Build autonomous and semi-autonomous AI agents using AutoGen or CrewAI to solve complex business logic.
• Backend & APIs: Develop and maintain scalable REST APIs using FastAPI or Flask to serve AI models and services.
• Data Architecture: Manage and optimize data retrieval using Elasticsearch, NoSQL databases, and Graph databases like Neo4j.
• LLMOps & MLOps: Establish robust pipelines for model monitoring, evaluation, and deployment to ensure high performance and reliability.
• Full-Stack Integration: Collaborate with front-end teams to integrate AI features into React/Node.js environments.
Required Technical Skills:
• Languages: Expert-level Python (strong hands-on coding) and advanced SQL.
• Frameworks: LangChain, LlamaIndex, LangGraph, AutoGen, or CrewAI.
• Databases: ElasticSearch, NoSQL, and Neo4j.
• AI/ML: Solid foundation in Machine Learning, Deep Learning, and LLM fine-tuning/prompt engineering.
• Backend: Proven experience with FastAPI or Flask for production APIs.
• Web: Familiarity with React and Node.js for full-stack AI integration.
Qualifications:
• Minimum 2+ years of experience as an AI Engineer or in a similar specialized Machine Learning role.
• Proven track record of deploying LLM-based applications to production.
• Strong understanding of vector embeddings, semantic search, and RAG architectures.
• Experience with Cloud environments (AWS/GCP/Azure) and CI/CD for ML (MLOps).
Skills: Digital : Amazon Web Service(AWS) Cloud Computing~Digital : Docker~Digital : Kubernetes
Experience Required: 6-8, Project Code :

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