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Apprentice Machine Learning Testing Jobs in Lehi, UT

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning, data visualization, SQL, Python or R programming, hypothesis testing, and communication of data ...

Strong quantitative background with applied statistics skills (distributions, hypothesis testing, regression, etc.) * Familiarity with predictive modeling, machine learning, generative AI, and data ...

Strong quantitative background with applied statistics skills (distributions, hypothesis testing, regression, etc.) * Familiarity with predictive modeling, machine learning, generative AI, and data ...

Strong quantitative background with applied statistics skills (distributions, hypothesis testing, regression, etc.) * Familiarity with predictive modeling, machine learning, generative AI, and data ...

General Road Construction Apprentice

Draper, UT · On-site

$15 - $18.75/hr

May be required to complete drug testing and physical abilities testing. * Must have a valid driver ... May be exposed to wet or humid conditions, moving machinery, outdoor weather, and extreme ...

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... testing strategies, evaluation frameworks, and governance controls to promote reliable, ethical ...

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Apprentice Machine Learning Testing information

See Lehi, UT salary details

$10

$18

$26

How much do apprentice machine learning testing jobs pay per hour?

As of May 31, 2026, the average hourly pay for apprentice machine learning testing in Lehi, UT is $18.17, according to ZipRecruiter salary data. Most workers in this role earn between $15.34 and $19.86 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Apprentice Machine Learning Testing, and why are they important?

To thrive as an Apprentice in Machine Learning Testing, a foundational understanding of statistics, programming (especially Python), and basic machine learning concepts is essential, often supported by a degree or coursework in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, Jupyter Notebooks, and version control systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help apprentices collaborate and identify testing issues efficiently. These skills ensure accurate model validation, effective troubleshooting, and contribute to the robust deployment of machine learning solutions.

What kinds of projects or tasks can I expect to work on as an Apprentice Machine Learning Testing?

As an Apprentice Machine Learning Testing, you’ll typically assist in evaluating machine learning models by designing and running tests, analyzing model outputs, and helping identify issues like bias or overfitting. You may work closely with data scientists and software engineers to validate model performance and ensure results align with project objectives. Your daily tasks might include preparing test datasets, executing automated testing scripts, and documenting findings to help improve model reliability. This role often serves as a valuable introduction to practical machine learning workflows and quality assurance processes in technical teams.

What does an Apprentice Machine Learning Testing do?

An Apprentice Machine Learning Testing professional assists in evaluating and validating machine learning models to ensure they perform as expected. They typically work under the guidance of experienced data scientists or engineers, running tests, analyzing results, and helping to identify issues such as bias or inaccuracies in algorithms. Their responsibilities may also include developing test cases, writing reports, and learning about data preprocessing and evaluation metrics. This role is ideal for those who are new to the field and want to build foundational skills in machine learning quality assurance.

What is the difference between Apprentice Machine Learning Testing vs Machine Learning Engineer?

AspectApprentice Machine Learning TestingMachine Learning Engineer
Required CredentialsBasic understanding of ML concepts, often pursuing relevant certifications or degreesAdvanced degrees (BSc, MSc, PhD) in CS or related fields, with extensive experience
Work EnvironmentEntry-level, supervised testing environments, often in training programsFull-time, independent development and deployment of ML models in production
Employer & Industry UsageInternships, training programs, entry-level roles in tech companiesEstablished tech firms, startups, research institutions

Apprentice Machine Learning Testing roles focus on learning and assisting with testing ML models under supervision, while Machine Learning Engineers design, build, and deploy ML systems independently. The apprentice position is ideal for gaining foundational skills, whereas the engineer role requires advanced expertise and experience.

What are popular job titles related to Apprentice Machine Learning Testing jobs in Lehi, UT? For Apprentice Machine Learning Testing jobs in Lehi, UT, the most frequently searched job titles are:
What job categories do people searching Apprentice Machine Learning Testing jobs in Lehi, UT look for? The top searched job categories for Apprentice Machine Learning Testing jobs in Lehi, UT are:
AI Engineer / Applied Data Scientist

AI Engineer / Applied Data Scientist

Kforce Technology Staffing

Draper, UT

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 8 days ago


Job description

RESPONSIBILITIES:
Kforce has a client that is seeking an AI Engineer/Applied Data Scientist in Draper, UT.
Overview:
In this role, you will operate at the intersection of AI engineering and applied data science. You will design, build, and deploy machine learning, generative AI, and agentic AI systems that power real-world products and decision-making at scale.
You will work end-to-end-from data exploration and modeling through production deployment-partnering closely with product, engineering, and business stakeholders to deliver measurable, reliable, and responsible AI outcomes.
Duties Include:
* 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, and 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
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
* Experience working cross-functionally in Agile environments, with clear and thorough documentation practices
* 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
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.