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Full Time Edge Ai Machine Learning Jobs (NOW HIRING)

Honeywell

Director Embedded AI Engineering

Atlanta, GA · On-site

$126K - $166K/yr

You will leverage your skills in edge optimization, system and embedded knowledge, AI/machine learning, MLOps, computer vision, innovation, and problem solving to drive advanced AI solutions. We are ...

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA

$140K - $170K/yr

It is a cutting-edge research and development opportunity with the potential to improve people ... Develop new advanced algorithms using, machine learning techniques, deep learning models, digital ...

Huzzle

AI Engineer - Remote

Role Type: Full-time Engagement: Independent Contractor Job Summary We are looking for a skilled AI ... edge AI initiatives. This is an exciting opportunity for someone passionate about machine learning ...

SentinelOne

AI/Machine Learning Engineer Intern

$31/hr

As an AI/Machine Learning Engineer Intern , you will be tasked with applying software engineering skills to create reliable, AI-powered products within a fast-paced product engineering environment.

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All JobsFull Time Edge Ai Machine Learning Jobs

Full Time Edge Ai Machine Learning information

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$25.5K

$42.6K

$88K

How much do full time edge ai machine learning jobs pay per year?

As of Jun 8, 2026, the average yearly pay for full time edge ai machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are Full Time Edge AI Machine Learning jobs?

Full Time Edge AI Machine Learning jobs involve developing and deploying machine learning models that run directly on edge devices, such as smartphones, IoT devices, or embedded systems, rather than relying solely on cloud computing. Professionals in these roles work on optimizing algorithms for low-power, resource-constrained environments and enabling real-time AI processing at the device level. These jobs typically require expertise in AI, machine learning, embedded systems, and sometimes hardware integration, and are essential for applications like smart cameras, autonomous vehicles, and industrial automation.

What are the key skills and qualifications needed to thrive as a Full Time Edge AI Machine Learning Engineer, and why are they important?

To thrive as a Full Time Edge AI Machine Learning Engineer, you need a solid background in computer science, machine learning algorithms, and embedded systems, often supported by a relevant degree and experience in AI model deployment. Familiarity with frameworks like TensorFlow Lite, ONNX, and hardware platforms such as NVIDIA Jetson or ARM Cortex is typically required, along with knowledge of programming languages like Python and C++. Strong problem-solving, adaptability, and effective communication skills help you collaborate across multidisciplinary teams and address real-time deployment challenges. These combined skills ensure efficient, scalable, and robust AI solutions on resource-constrained edge devices, which is critical for success in this rapidly evolving field.

What is the difference between Full Time Edge Ai Machine Learning vs Data Scientist?

AspectFull Time Edge Ai Machine LearningData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; experience with ML frameworksBachelor's or Master's in CS, Statistics, or related fields; strong programming skills
Work EnvironmentEdge devices, IoT environments, real-time data processingOffice or remote, data analysis, model development
Industry UsageAI hardware companies, IoT, autonomous systemsTech, finance, healthcare, research

Full Time Edge Ai Machine Learning specialists focus on deploying ML models on edge devices for real-time processing, often requiring knowledge of hardware and embedded systems. Data Scientists analyze data, develop models, and interpret results primarily in cloud or office settings. While both roles involve machine learning, Edge AI emphasizes deployment on hardware, whereas Data Scientists focus on data analysis and model development.

What are some common challenges faced when deploying AI models on edge devices in a full-time Edge AI Machine Learning role?

One of the main challenges in this role is optimizing machine learning models to run efficiently on resource-constrained edge devices, which often have limited processing power and memory compared to cloud environments. Ensuring low latency and real-time performance without sacrificing accuracy requires specialized techniques such as model quantization or pruning. Additionally, maintaining robust security and handling data privacy on distributed devices adds complexity. Collaboration with hardware engineers and software developers is frequently required to address these multidisciplinary challenges.
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Full Time Edge Ai Machine Learning jobs near you
Infographic showing various Full Time Edge Ai Machine Learning job openings in the United States as of May 2026, with employment types broken down into 86% Full Time, and 14% Part Time. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
AI/Machine Learning Engineer - Python - Loops

AI/Machine Learning Engineer - Python - Loops

IFS

Palo Alto, CA • On-site

Full-time

Posted 5 days ago

Job description

Job Summary:
IFS is a billion-dollar revenue company with over 7000 employees, renowned for its cutting-edge AI technology that enhances enterprise software solutions. The AI/Machine Learning Engineer will design and optimize backend systems, develop Python services, and integrate AI/ML capabilities into enterprise workflows.
Responsibilities:
• Build and maintain Python-based services, integrations, and data pipelines that support AI agent functionality.
• Develop reusable libraries, APIs, and frameworks to accelerate AI-driven product capabilities.
• Ensure code quality, maintainability, and scalability through testing, CI/CD, and performance monitoring.
• Implement and optimize workflows leveraging LLMs, embeddings, RAG systems, and vector databases.
• Integrate AI/ML libraries and external APIs (e.g., OpenAI, Hugging Face, LangChain, Pinecone, Weaviate).
• Experiment with prompt engineering and fine-tuning to improve reliability and performance of deployed agents.
• Partner with product and core engineering teams to translate requirements into technical solutions.
• Contribute to architecture decisions and internal technical documentation.
• Support the deployment of agents into enterprise environments with a focus on stability, accuracy, and scale.
Qualifications:
Required:
• 2–5 years of professional experience as a Python Engineer / Backend Engineer (experience with AI/ML is a strong plus).
• Strong proficiency in Python and familiarity with JavaScript/TypeScript for integrations.
• Hands-on knowledge of AI/ML frameworks and tools (OpenAI, Hugging Face, LangChain, vector DBs, RAG).
• Understanding of system integration patterns and comfort working with RESTful APIs, JSON, and data pipelines.
• Strong debugging, testing, and optimization skills.
• Ability to write clean, maintainable, and well-documented code.
Preferred:
• Experience with enterprise systems (CRM, ERP, Helpdesk, Developer platforms, HR/Finance systems) is a plus.
Company:
IFS develops and delivers enterprise software solutions such as ERP, EAM, Service Management and Industrial AI It is a sub-organization of EQT. Founded in 1983, the company is headquartered in Linköping, SWE, with a team of 5001-10000 employees. The company is currently Late Stage.

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