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Machine Learning Engineer Jobs in St George, UT (NOW HIRING)

beatBread

AI Engineer

Saint George, UT · On-site

$50K - $90K/yr

About The Role As an AI Engineer, you will design, build, and deploy sophisticated AI solutions ... Collaborating closely with cross-functional teams, you will develop and optimize machine learning ...

beatBread

AI Engineer

Saint George, UT · On-site

About The Role As an AI Engineer, you will design, build, and deploy sophisticated AI solutions ... Collaborating closely with cross-functional teams, you will develop and optimize machine learning ...

DataAnnotation

DevOps Engineer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

DataAnnotation

Software Engineer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

DataAnnotation

Frontend Engineer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

DataAnnotation

Mobile Software Engineer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

DataAnnotation

Backend Software Engineer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

DataAnnotation

Senior Software Engineer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

DataAnnotation

Staff Software Engineer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

DataAnnotation

Full Stack Engineer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

DataAnnotation

QA Engineer - AI Trainer

Saint George, UT · Remote

$40 - $75/hr

As a member of DataAnnotation's coding team, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers ...

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Machine Learning Engineer information

See St George, UT salary details

$29.7K

$121.5K

$182.6K

How much do machine learning engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for machine learning engineer in St. George, UT is $121,495.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,800.00 and $146,200.00 per year, depending on experience, location, and employer.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What jobs make $3,000 a month without a degree?

A Machine Learning Engineer typically requires a degree, but roles such as data annotator, technical support specialist, or freelance programmer can sometimes earn around $3,000 monthly without a formal degree, especially with relevant skills and experience. These jobs often involve self-taught skills, online certifications, or on-the-job training and may require proficiency in tools like Python or cloud platforms.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What cities near St. George, UT are hiring for Machine Learning Engineer jobs? Cities near St. George, UT with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
AI Engineer

AI Engineer

beatBread

Saint George, UT • On-site

$50K - $90K/yr

Full-time

Posted 29 days ago

Job description

About The Role
As an AI Engineer, you will design, build, and deploy sophisticated AI solutions that drive innovation and strategic impact across the organization. Collaborating closely with cross-functional teams, you will develop and optimize machine learning models, refine data pipelines, and harness emerging AI technologies such as ChatGPT and OpenAI APIs. This role offers the opportunity to shape and implement AI-driven strategies, leveraging your expertise in Python, SQL, Docker, AWS, and custom ML training techniques.
Essential Duties and Responsibilities
  • Lead the end-to-end development, testing, and deployment of AI and ML models
  • Architect and maintain scalable data pipelines using Python, SQL, and AWS services
  • Integrate and fine-tune ChatGPT and OpenAI APIs to meet specific business needs
  • Build and manage containerized environments and workflows with Docker
  • Collaborate with data scientists and engineers to optimize model performance and reliability
  • Implement, monitor, and improve model training processes, including domain-specific custom training
  • Advise on and adopt new AI frameworks and best practices to enhance the organization's AI capabilities
  • Troubleshoot and resolve performance and security issues related to AI systems

Qualifications and Skills
  • Bachelor's degree in Computer Science, Data Science, Engineering, or related field, or equivalent experience.
  • 4-8 years of experience in AI engineering, data science, software engineering, software development, or related roles.
  • Proficiency in Python for data manipulation, scripting, and model development
  • Solid understanding of SQL for data querying, management, and optimization
  • Hands-on experience with Docker for containerization and deployment
  • Familiarity with ChatGPT, OpenAI APIs, and other large language models
  • Strong knowledge of machine learning fundamentals (supervised/unsupervised learning, model tuning, evaluation)
  • Experience working with AWS (e.g., S3, EC2, Lambda) for cloud-based solutions
  • Ability to collaborate effectively with cross-functional stakeholders
  • Proven track record of learning and adapting to emerging AI technologies and tools

Physical Requirements
  • Flexible to work additional hours as needed to meet project deadlines.
  • Ability to sit and work at a computer for extended periods.
  • Occasional lifting of equipment or materials may be required, but not exceeding 20 pounds.
  • Ability to communicate effectively in person, over the phone, and through digital channels.

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