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Embedded Machine Learning Engineer Jobs in Arizona

Varsity Tutors

Machine Learning Tutor

Phoenix, AZ · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Tempe, AZ · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Glendale, AZ · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Mesa, AZ · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Scottsdale, AZ · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Chandler, AZ · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Tucson, AZ · Remote

$18 - $40/hr

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

Varsity Tutors

Machine Learning Tutor

Gilbert, AZ · Remote

$18 - $40/hr

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

Axon

Senior Machine Learning Scientist

Scottsdale, AZ · On-site

$92K - $125K/yr

What You'll Do Location: any cities with Axon Engineering Hub in US, Vietnam, EU (see * US: Seattle ... IoT devices, or embedded systems is highly desirable. * Excellent problem-solving skills ...

Insero

Embedded Software Engineer

Tempe, AZ · On-site

$70K - $110K/yr

We are proud to design precise machine control and guidance, cutting edge wireless installations ... embedded engineering. You will be welcomed into our support team, experience a good work/life ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Arizona

Embedded Machine Learning Engineer information

See Arizona salary details

$65.2K

$142.9K

$162.1K

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

As of Jul 8, 2026, the average yearly pay for embedded machine learning engineer in Arizona is $142,936.00, according to ZipRecruiter salary data. Most workers in this role earn between $122,500.00 and $161,200.00 per year, depending on experience, location, and employer.

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 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 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 are popular job titles related to Embedded Machine Learning Engineer jobs in Arizona? For Embedded Machine Learning Engineer jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Arizona look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Arizona are:
What cities in Arizona are hiring for Embedded Machine Learning Engineer jobs? Cities in Arizona with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you
Infographic showing various Embedded Machine Learning Engineer job openings in Arizona as of July 2026, with employment types broken down into 1% Internship, 91% Full Time, 6% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $142,936 per year, or $68.7 per hour.

Machine Learning Operations Manager

Globe Telecom, Inc.

Globe, AZ • On-site

Full-time

Posted 7 days ago

Job description

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.

Job Description The MLOps Manager role is all about leading and managing the deployment, management, maintenance and optimization of machine learning models in production environments.

DUTIES AND RESPONSIBILITIES:

  • Team Leadership - provide mentorship, guidance and support to team members

  • Strategic Planning - develop and execute MLOps strategy aligned with Globe's objectives

  • Model Deployment and Management - oversee the deployment of Machine Learning models into production and ensures reliability, scalability and performance. Optimize the models to make it cost effective .

  • Infrastructure knowledge - evaluate and select appropriate infrastructure, tools and technologies to support end-to-end machine learning lifecycle

  • Automation and Orchestration - develop or oversee the development of pipelines for model inference and retraining

  • Collaboration - collaborate with data scientists, data engineers, insighters and other stakeholders to identify improvements in the models.

  • Model Governance - guides the implementation of alerting system or dashboards for tracking the health, performance and reliability of models in production and ensures compliance with regulations, privacy policies and standards

  • Continuous Improvement - drive continuous improvement initiatives for the enhancement of deployed models and MLOps practices

REQUIREMENTS:

  • Minimum of 5 years of experience in machine learning, data science, or software engineering roles.

  • At least 2-3 years of experience in MLOps, DevOps, or similar roles, with a focus on model deployment and operationalization

  • Proven track record of managing projects and leading teams.

    Knowledge of data privacy regulations and best practices in model governance and security.

    Willingness to continuously learn and adapt to new technologies and methodologies in the MLOps domain.

  • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.

Soft Skills:

  • Excellent communication and interpersonal skills, with the ability to collaborate with cross-functional teams and translate technical concepts into business terms.

  • Strong problem-solving abilities and analytical thinking

Hard Skills:

  • Proficiency in programming languages such as Python, R, or Java.

  • Experience with cloud platforms (AWS, Azure, Google Cloud) and containerization technologies (Docker, Kubernetes).

  • Strong understanding of CI/CD pipelines, version control (e.g., Git), and infrastructure as code (IaC).

Equal Opportunity Employer
Globe's hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.
Globe's Diversity, Equity and Inclusion Policy Commitment can be accessed here

Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.

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