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Full Time Edge Ai Machine Learning Jobs (NOW HIRING)

Samsara

Staff Machine Learning Engineer - Edge AI

... Machine Learning Engineer. * Profound experience in optimizing ML models and systems for Edge ... The range of annual base salary for full-time employees for this position is below. Please note ...

Fireworks AI

Applied Machine Learning Engineer

New York, NY · On-site

$170K - $240K/yr

As an Applied Machine Learning Engineer, you will serve as a vital bridge between cutting-edge AI research and practical, real-world applications. Your work will focus on developing, fine-tuning, and ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA · On-site

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... It is a cutting-edge research and development opportunity with the potential to improve people ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... It is a cutting-edge research and development opportunity with the potential to improve people ...

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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 29, 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 is the synonym for full?

A synonym for full is complete or entire. In the context of a Full Time Edge AI Machine Learning role, it indicates a position with full-time hours, typically 40 hours per week, requiring consistent commitment and often involving comprehensive responsibilities.

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.

Is it full or ful?

The correct term for the job is 'Full Time Edge AI Machine Learning' position. It indicates a full-time role, typically requiring standard working hours and possibly involving skills in machine learning, AI tools, and programming. The abbreviation 'ful' is not used in this context and may be a typo or shorthand for 'full.'
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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 June 2026, with employment types broken down into 2% As Needed, 84% Full Time, 4% Part Time, 1% Temporary, and 9% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Director, AI & Machine Learning

Director, AI & Machine Learning

FFF Enterprises

Flower Mound, TX • On-site

Full-time

Posted 27 days ago

Key responsibilities

  • Lead the development and execution of enterprise AI and intelligent automation strategy.

  • Build and lead a team responsible for delivering AI and intelligent automation solutions while ensuring compliance with governance and security standards.

  • Oversee the design, development, testing, and deployment of AI and automation solutions across business units.

Job description

Job Summary:
FFF Enterprises is a company focused on delivering innovative solutions, and they are seeking a Director of AI & Machine Learning to lead the strategy and execution of enterprise AI capabilities. The role involves building a team to deliver AI and automation solutions while ensuring compliance with governance and security standards.
Responsibilities:
• Lead the development and execution of enterprise AI and intelligent automation strategy.
• Define and implement the enterprise AI architecture using Databricks AI capabilities.
• Establish architectural standards for generative AI, machine learning models, and agent-based systems.
• Design frameworks for multi-agent AI systems that support business workflows and decision-making.
• Identify high-impact AI and automation opportunities across business units.
• Align AI initiatives with the enterprise data platform and overall technology strategy.
• Build and lead a high-performing team responsible for delivering AI and intelligent automation solutions.
• Lead, mentor, and develop a team of AI engineers, machine learning engineers, and automation developers.
• Establish engineering standards and best practices for AI development and automation implementation.
• Oversee the design, development, testing, and deployment of AI and automation solutions.
• Coordinate work across AI engineering, data engineering, reporting teams, and business stakeholders.
• Lead the design and implementation of modern AI architectures including generative AI and agentic systems.
• Implement multi-agent AI architectures for automation and operational decision support.
• Deploy AI solutions leveraging Databricks capabilities including MLflow, vector search, and model serving.
• Implement Retrieval Augmented Generation (RAG) architectures using governed enterprise data.
• Lead adoption of conversational AI platforms including Databricks Genie and Microsoft Copilot integrations.
• Define the enterprise strategy for robotic process automation using Microsoft Power Automate.
• Identify business processes suitable for automation and prioritize initiatives based on operational impact.
• Oversee the design and development of automation workflows and automated business processes.
• Integrate AI models and agent-based systems into automation workflows to enable intelligent decision-making.
• Establish governance, monitoring, and reliability standards for automation solutions.
• Collaborate with business units to streamline processes and reduce manual effort through automation.
• Establish policies and standards for responsible AI and intelligent automation.
• Ensure compliance with enterprise security, privacy, and data governance policies.
• Implement evaluation frameworks to measure AI performance, accuracy, and reliability.
• Maintain transparency and auditability of automated and AI-driven decisions.
• Collaborate with Data Engineering teams to leverage curated enterprise data for AI solutions.
• Partner with Reporting and Analytics teams to embed AI outputs into business insights and decision tools.
• Work with business stakeholders to translate operational challenges into AI-enabled and automated solutions.
• Communicate AI and automation capabilities, risks, and value to both technical and non-technical audiences.
Qualifications:
Required:
• Bachelor’s Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field, or four (4) years relevant experience in lieu of degree.
• Advanced knowledge of artificial intelligence, machine learning, and generative AI technologies.
• Knowledge of agent-based AI architectures and multi-agent systems.
• Knowledge of robotic process automation (RPA) concepts and intelligent automation strategies.
• Understanding of enterprise AI governance, security, and model lifecycle management.
• At least 10 years of experience in artificial intelligence, machine learning, automation, or related technical roles, including 5 years of leadership experience managing technical teams.
• Strong leadership and team management capabilities.
• Expertise designing enterprise AI architectures and machine learning platforms.
• Experience implementing generative AI and agent-based systems.
• Experience with Databricks AI capabilities including MLflow and model serving.
• Experience integrating AI capabilities with Microsoft Copilot platforms.
• Strong strategic thinking and problem-solving abilities.
• Excellent verbal and written communication skills.
• Ability to translate business challenges into AI and automation solutions.
Preferred:
• Familiarity with Microsoft Copilot platforms and conversational AI technologies.
• Familiarity with Databricks AI platform capabilities including MLflow and vector search.
• Knowledge of vector databases, embeddings, and semantic AI architectures.
• At least 12–15 years of progressive experience in AI engineering, machine learning engineering, intelligent automation, or enterprise data platform development.
• Experience leading AI and automation initiatives using modern platforms such as Databricks, Microsoft Copilot, and Microsoft Power Automate is strongly preferred.
• Experience implementing robotic process automation using Microsoft Power Automate.
• Experience with conversational AI platforms such as Databricks Genie.
• Experience implementing multi-agent orchestration frameworks.
• Experience integrating AI and automation systems with enterprise applications such as ERP, CRM, or logistics platforms.
• Experience with AI-based video or image processing technologies.
Company:
FFF Enterprises is a Pharmaceuticals supplier of critical-care biopharmaceuticals, plasma products and vaccines. Founded in 1988, the company is headquartered in Temecula, USA, with a team of 501-1000 employees. The company is currently Late Stage.

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