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Machine Learning Engineer Jobs in Tooele, UT (NOW HIRING)

The Engineer will design, build, and deploy machine learning, generative AI, and agentic AI systems ... Design, build, and optimize machine learning models, including classification, regression ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

NGA AI Engineer Manager

Salt Lake City, UT · On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Sr. Data Engineer

Draper, UT · On-site

$107K - $128K/yr

Essential Job Duties As a Senior Data Engineer, you will play a key role in designing, building ... Design, build, and operationalize machine learning pipelines for training, validation, deployment ...

Responsibilities - Design and implement advanced AI and machine learning solutions - Analyze ... Engineering, Mathematics, Statistics, or a related quantitative field - At least 3 years of ...

CAD Engineer

Magna, UT · On-site +1

$60K - $80K/yr

... vision, machine learning, and generative AI within the automotive sector. With over $380M in ... We are seeking an experienced CAD Engineer to join the US Implementation Team. In this role, you ...

Sr. Software Engineer

Salt Lake City, UT

$118K - $156K/yr

Adopt various tools developed by AppBank Engineering team to automate failures using machine learning techniques and notify discrepancies in the health of production and automation of health ...

AI Engineer

Salt Lake City, UT · On-site +1

$101K - $159K/yr

... machine learning and deep learning models. Ability to build and deploy MCP servers to provide LLMs ... Systems Engineering - Preferred * Automation - Preferred * Test Automation - Preferred * Computer ...

Comscore, Total Visits, March 2025) Day to Day As a Software Engineer IV (ML) on the Machine Learning Model Platform team at Indeed, you will be responsible for leading and executing key objectives ...

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Machine Learning Engineer information

See Tooele, UT salary details

$29.6K

$120.9K

$181.7K

How much do machine learning engineer jobs pay per year?

As of Jun 21, 2026, the average yearly pay for machine learning engineer in Tooele, UT is $120,895.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,300.00 and $145,500.00 per year, depending on experience, location, and employer.

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

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-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies 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 they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

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 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.

What cities near Tooele, UT are hiring for Machine Learning Engineer jobs? Cities near Tooele, UT with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in Tooele, UT as of June 2026, with employment types broken down into 97% Full Time, 2% Part Time, and 1% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $120,895 per year, or $58.1 per hour.
AI Engineer

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 17 days ago


Job description

RESPONSIBILITIES:
Kforce has a client in Draper, UT that is seeking an AI Engineer who will operate at the intersection of AI engineering and applied data science. The Engineer will design, build, and deploy machine learning, generative AI, and agentic AI systems that power real-world products and decision-making at scale.
Duties:
* Design, build, and optimize machine learning models, including classification, regression, clustering, and recommendation systems
* Develop and productionize LLM-based solutions, including prompt engineering, retrieval-augmented generation (RAG) pipelines, fine-tuning, and multimodal models
* Build and orchestrate agentic AI workflows (LangGraph or similar), including tool usage, decision logic, and long-running agent execution
* Leverage AI-assisted development tools (e.g., Claude Code or similar) to accelerate software development, testing, and refactoring while maintaining high standards of quality and correctness
* Design and implement modular sub-agents and reusable tools, applying strong software engineering and data science principles across the agent lifecycle (design, build, evaluate, deploy, iterate)
* Apply embeddings and vector search techniques to enable NLP, semantic search, and retrieval use cases
* Process and analyze large-scale datasets using Python (pandas, scikit-learn, PySpark) and SQL
* Implement MLOps best practices, including CI/CD pipelines, model versioning, monitoring, evaluation, and reproducibility
* Evaluate model and LLM performance in production using offline, online, and incremental evaluation strategies
* Translate complex analytical results into clear, actionable insights for both technical and non-technical stakeholders
* Stay current with emerging trends in AI, ML, generative AI, and agentic systems, and apply them pragmatically to business challenges
REQUIREMENTS:
* Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related quantitative field
* 2+ years of hands-on experience in data science, machine learning engineering, or applied AI within a fast-paced, production-oriented environment
* Advanced proficiency in Python, including experience with pandas, scikit-learn, and PySpark
* Strong SQL skills for large-scale data analysis and feature engineering
* Proven experience building, tuning, and evaluating machine learning models, with a solid understanding of evaluation metrics and tradeoffs
* Experience with vector embeddings, similarity search, and retrieval pipelines
* Practical experience with LLMs, including prompt engineering, API/SDK integration, multimodal models, and fine-tuning approaches
* Hands-on experience with agentic development frameworks (LangGraph preferred or equivalent), including orchestration patterns, sub-agents, and tool integration
* Experience using AI-assisted (-agentic coding-) development tools, with strong engineering judgment around correctness, testing, and maintainability
* Understanding of the agentic software lifecycle, including evaluation, observability, failure modes, and iterative improvement in production environments
* Familiarity with responsible AI principles, including bias, fairness, and governance in deployed systems
* Ability to translate business problems into scalable AI/ML solutions and communicate effectively across technical and non-technical audiences
* Familiarity with model deployment and MLOps practices, including CI/CD, monitoring, and reproducibility
Nice to Have:
* Experience operating and scaling agentic AI systems in production environments
* Background in recommendation systems, optimization, or decision intelligence
* Experience building and delivering AI-powered products (beyond prototyping or research environments)
The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.
We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.
Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless and until paid and may be modified in its discretion consistent with the law.
This job is not eligible for bonuses, incentives or commissions.
Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.
By clicking ?Apply Today? you agree to receive calls, AI-generated calls, text messages or emails from Kforce and its affiliates, and service providers. Note that if you choose to communicate with Kforce via text messaging the frequency may vary, and message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You will always have the right to cease communicating via text by using key words such as STOP.