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Apprentice Machine Learning Testing Jobs in Lehi, UT

Jobtailor

Data Scientist

Lehi, UT · On-site

$90 - $130/hr

Machine Learning ATS Optimization Keywords Hard Skills * Data Science * Statistical Methods * Model Evaluation * Data Cleaning * Data Transformation * Pattern Identification * Hypothesis Testing

Waystar

Sr. Data Scientist

Lehi, UT · On-site

... machine learning models including: hold-out sets, cross-validation, leave-one-out testing • Understanding of orders of algorithms and how they scale • Demonstrated competency in R/Python ...

Waystar

Sr. Data Scientist

Lehi, UT · On-site

Complete familiarity with empirical approaches to estimate performance of machine learning models including: hold-out sets, cross-validation, leave-one-out testing * Understanding of orders of ...

Crew Career Center

Sr. Data Scientist

Lehi, UT · On-site

Complete familiarity with empirical approaches to estimate performance of machine learning models including: hold-out sets, cross-validation, leave-one-out testing * Understanding of orders of ...

Patientco

Sr. Data Scientist

Lehi, UT · On-site

Complete familiarity with empirical approaches to estimate performance of machine learning models including: hold-out sets, cross-validation, leave-one-out testing * Understanding of orders of ...

Patientco

Data Scientist

Lehi, UT · On-site

Complete familiarity with empirical approaches to estimate performance of machine learning models including: hold-out sets, cross-validation, leave-one-out testing * Understanding of orders of ...

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Apprentice Machine Learning Testing information

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

As of Aug 28, 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 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 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 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 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:

Apprentice Machine Learning Testing jobs near you

Senior Machine Learning Engineer

Limelight Health

South Jordan, UT • On-site

$110 - $140/hr

Other

Medical, Dental, Vision, Retirement, PTO

Posted 9 days ago

Job description

Location

Tysons Corner, VA; South Jordan, UT

Employment Type

Full time

Department

Technology

Strider Technologies delivers strategic intelligence that helps organizations make faster, more confident decisions in an increasingly complex global environment. Using cutting‑edge AI and proprietary methodologies, we transform open‑source data into actionable insights that help protect technology, talent, and supply chains from nation‑state risks.

We’re looking for a Senior Machine Learning Engineer to help Strider unlock value from large‑scale document collections. In this role, you’ll build production‑ready AI/ML systems for document classification, prioritization, and other document‑processing workflows. Your work will directly support the development of scalable intelligence products that help users find meaningful signals in complex, high‑volume data.

You’ll work with real‑world data and collaborate closely with intelligence and engineering teams to turn complex multilingual information into actionable insights. This role is a strong fit for someone who enjoys technical ownership, builds maintainable systems, and excels at translating ambiguity into measurable product impact.

You will:
  • Design, build, and maintain scalable machine learning solutions, typically focused on document classification tasks using AI/ML models.
  • Work across the full engineering lifecycle from exploratory analysis and prototype development through production deployment, monitoring, iteration, and operational ownership.
  • Take models from R&D into production and deploy them using AWS cloud services.
  • Ensure the reliability and performance of machine learning applications by carrying out continuous testing and optimization.
  • Use AI coding tools such as Cursor and Claude to improve development velocity while maintaining high standards for code quality, reliability, and maintainability.
  • Participate in design reviews, code reviews, and team discussions, providing technical leadership and insight.
What you need to be successful:
  • Bachelor’s degree in Computer Science, Engineering, or a related field.
  • 5+ years of experience in Machine Learning or AI.
  • Strong Python skills and sound software engineering judgment, with the ability to write maintainable, production‑oriented code.
  • Experience working with large, complex datasets to assess quality, coverage, value, and tradeoffs.
  • Experience deploying and operating ML or data‑processing pipelines in production environments using AWS.
  • Strong understanding of NLP techniques such as tokenization, entity extraction, disambiguation, and language models.
  • Strong communication skills, with the ability to explain complex technical concepts to engineering partners, product stakeholders, and leadership.
  • Self‑motivated, pragmatic, and impact‑oriented, with strong problem‑solving skills and attention to detail.
Nice-to-haves:
  • Strong AWS skills.
  • Experience with Elasticsearch.
  • Experience working with NLP in foreign (non‑English) languages.
  • Familiarity with MLOps tools and practices.
  • Experience with PyTorch or Scikit‑Learn.
  • Master’s degree or PhD in Computer Science, Engineering, or related field.
Benefits:
  • Competitive Compensation
  • Company Equity Options
  • Flexible PTO
  • Wellness Reimbursement
  • US Holidays (office closed)
  • Paid Parental Leave
  • Comprehensive Medical, Dental, and Vision Insurance
  • 401(k) Plan

Strider is an equal opportunity employer. We are committed to fostering an inclusive workplace and do not discriminate against employees or applicants based on race, color, religion, gender, national origin, age, disability, genetic information, or any other characteristic protected by applicable law. We comply with all relevant employment laws in the locations where we operate. This commitment applies to all aspects of employment, including recruitment, hiring, promotion, compensation, and professional development.

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