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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

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 ...

NICE

Senior Software Engineer, Actimize

Santa Clara, UT

$109K - $144K/yr

Senior Software Engineer - Fraud Detection Platform About the Role We are looking for a Lead Software Engineer to maintain and evolve the web applications that power our fraud detection platform. The ...

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in St. George, UT

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 Jul 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 engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

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 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.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

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 engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

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 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
Infographic showing various Machine Learning Engineer job openings in St. George, UT as of July 2026, with employment types broken down into 92% Full Time, 5% Part Time, and 3% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $121,495 per year, or $58.4 per hour.
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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