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Embedded Machine Learning Engineer Jobs in Utah (NOW HIRING)

Tagup

Machine Learning Engineer

Salt Lake City, UT

$125K - $165K/yr

We are seeking a Machine Learning Engineer to join our Salt Lake City team and help shape the next generation of AI-driven defense and aviation systems. In this role, you'll go beyond building models ...

BILL

Senior Machine Learning Engineer

Draper, UT · On-site

$97.70K - $134.20K/yr

As a Senior Machine Learning Engineer , you'll play a pivotal role in designing, building, and deploying machine learning solutions that power BILL's next-generation products. This is an opportunity ...

BILL

Senior Machine Learning Engineer

Draper, UT · On-site

$114.50K - $151K/yr

As a Senior Machine Learning Engineer, you will design, build, and deploy machine learning solutions that enhance BILL's products and directly impact user experiences. Responsibilities : • Design ...

BILL

Senior Machine Learning Engineer

Draper, UT · On-site

$145.70K - $174.80K/yr

As a Senior Machine Learning Engineer , you'll play a pivotal role in designing, building, and deploying machine learning solutions that power BILL's next-generation products. This is an opportunity ...

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$63.7K

$139.6K

$158.4K

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

As of May 29, 2026, the average yearly pay for embedded machine learning engineer in Utah is $139,636.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,700.00 and $157,500.00 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 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 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 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 Utah? For Embedded Machine Learning Engineer jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Utah look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Utah are:
What cities in Utah are hiring for Embedded Machine Learning Engineer jobs? Cities in Utah with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you
Machine Learning Engineer

Machine Learning Engineer

Tagup

Salt Lake City, UT

$125K - $165K/yr

Full-time

Posted 11 days ago

Job description

Tagup is a defense technology company founded at MIT that is delivering logistics decision advantage with next-generation AI. We’re growing rapidly and are looking for change-makers passionate about delivering innovative technologies to solve the most challenging problems in the world’s highest stakes environments. This is an exciting opportunity to engage in meaningful work that strengthens national security and contributes to the success of U.S. and allied forces. Join us in shaping the future of defense logistics for a safer tomorrow.


We are seeking a Machine Learning Engineer to join our Salt Lake City team and help shape the next generation of AI-driven defense and aviation systems. In this role, you’ll go beyond building models — you’ll design, deploy, and scale AI solutions that directly support mission-critical operations.

This is not a typical ML position. You’ll work at the intersection of cutting-edge research and real-world application, creating models and infrastructure that deliver measurable improvements in reliability, efficiency, and performance.

If you’re motivated by solving complex technical challenges and want your work to make a tangible impact on national security and aviation safety, we want to hear from you.
What You’ll Do
  • Develop, train, and optimize ML models for large-scale applications.
  • Build pipelines for data ingestion and model deployment.
  • Work with engineers and subject-matter experts to refine solutions.
  • Conduct testing and validation to ensure reliability.
  • Co-author technical reports on data analysis and model performance.
  • Continuously improve ML infrastructure and workflows.
  • Collaborate with customers to identify new data sources and the industrial processes they will support; some customer travel may be required.
What We’re Looking For
  • 4+ years of machine learning experience, with strong Python skills and proficiency in frameworks such as PyTorch or TensorFlow.
  • Proven ability to deploy ML models into production and work with large, complex datasets.
  • Hands-on experience with MLOps tools and practices, including Kubernetes, MLflow, and CI/CD pipelines.
  • Experience building and managing cloud infrastructure as code (AWS, Azure, or GCP) with tools such as Terraform or Ansible.
  • Familiarity with datastores (MySQL, Postgres, or MongoDB) and prior exposure to aviation, defense, or other safety-critical environments is a plus.
Salary

The estimated salary range for this position is between $125,000 and $165,000 annually. We strive to provide a competitive salary and benefits package that aligns with our employees’ experience and qualifications. Our primary objective is to attract and retain top talent, and we firmly believe in compensating our employees fairly for their invaluable contributions.

As a rapidly expanding technology company, we extend part-ownership to all team members through an Employee Stock Option Plan. Additionally, we offer comprehensive health insurance benefits, access to the company’s 401K plan, and foster a team-oriented work environment with regular company outings!

Why Join Us?

This is your opportunity to move beyond academic experiments and build AI models that make a real difference in defense and industry. At Tagup, you’ll work with a world-class, agile team in a supportive environment that encourages rapid iteration and continuous learning.

Tagup is an equal opportunity employer and individuals seeking employment with us are considered without regard to race, color, religion, national origin, age, sex, marital status, physical or mental disability, veteran status, gender identity, sexual orientation, or any other characteristic protected by law.

Citizenship: Due to the nature of our work with the U.S. Department of Defense, applicants must be authorized to work for any employer in the U.S. We are unable to sponsor visas at this time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.