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Apprentice Machine Learning Testing Jobs in Missouri

Jobtailor

Machine Learning Scientist II

California, MO · On-site

$120 - $180/hr

A/B Testing * Causal Inference * Deep Learning * Problem Formulation * Evaluation Strategy Soft Skills * Collaboration * Communication Industry Keywords * AI-Driven Systems * Machine Learning ...

New

iHerb

Principal Machine Learning Engineer

California, MO · On-site

$205 - $230/hr

... testing, integration testing, job schedulers, cloud technologies like AWS Lambda and Google ... machine learning or machine learning systems/infrastructure, and one (1) year of relevant work ...

6AM City, LLC

Machine Learning Engineer

California, MO · On-site

$110 - $170/hr

As a Machine Learning Integration Engineer, you will help rapidly prototype, mature, and monitor ML ... Contribute to the research, design, implementation, and testing of CV and/or AI/ML software

Jobtailor

Software Engineer, ML Platform

California, MO · On-site

$140 - $200/hr

Develop new deployment patterns for machine learning models with CI/CD pipelines and automated testing * Apply AI tools in the engineering workflow and bring an AI-native lens to engineering and ...

New

Jobgether

$225K - $250K/yr

Improve the reliability, maintainability, testing, and overall engineering quality of the machine learning platform. * Establish technical standards, modeling practices, and engineering best ...

Third-Party Job Posts

Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing). * Great understanding of machine learning principles ...

Jobgether

Partner with machine learning engineers and researchers to transition new models and approaches from prototypes into production-ready systems. * Develop automation, testing, observability, monitoring ...

New

Kohl's

$95K - $131K/yr

Contribute to the roadmap for Machine Learning Engineering and Data Science tools, including ... Support development and maintain monitoring, alerting, and automated testing frameworks to ensure ...

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Apprentice Machine Learning Testing information

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 Missouri?

For Apprentice Machine Learning Testing jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Apprentice Machine Learning Testing jobs in Missouri look for?

The top searched job categories for Apprentice Machine Learning Testing jobs in Missouri are:

What cities in Missouri are hiring for Apprentice Machine Learning Testing jobs?

Cities in Missouri with the most Apprentice Machine Learning Testing job openings:

Apprentice Machine Learning Testing jobs near you

Machine Learning Scientist II

Jobtailor

California, MO • On-site

$120 - $180/hr

Other

Posted 3 days ago

New

Job description

Design & Implement ML Solutions: Take ownership of the end-to-end ML lifecycle for your projects, from ideation and research to deployment and monitoring.

Test, Learn, and Iterate: Design and analyze tests to validate your models and quantify their business impact and design future iterations.

Collaborate and Communicate: Partner closely with product managers, engineers, and business stakeholders to understand requirements, define problems, and communicate your findings and results effectively.

Requirements
  • Bachelor’s degree in Computer Science or a related technical field; or Equivalent related professional experience.
  • 1+ years of relevant professional experience.
  • Proven ability to design end-to-end ML solutions, including problem formulation, identification and preparation of data sources, algorithm selection, feature engineering, evaluation strategy, and production deployment and monitoring.
  • Strong programming skills in Python and its data science ecosystem (such as pandas, scikit-learn, PySpark) and proficiency in SQL, with experience following software engineering best practices and contributing to shared codebases.
  • Familiarity with AI-driven systems, tools, or workflows and applying AI/ML concepts to real world products, including experience with the machine learning software development lifecycle from experimentation through operational monitoring.
  • Expertise in applied ML: Deep, practical knowledge of machine learning theory (supervised/unsupervised learning, deep learning) and statistical modeling and a strong command of experimental design (A/B testing) and causal inference to accurately measure impact.
  • Able to design end-to-end ML solutions: framing the problem, choosing data sources, selecting algorithms, and defining evaluation strategy.
  • Technical Fluency: Strong programming skills in Python and its data science ecosystem (e.g., pandas, scikit-learn, pySpark), plus proficiency in SQL.
Core Competencies

Demonstrates expertise in designing and implementing end-to-end machine learning solutions, including problem formulation, data preparation, algorithm selection, and evaluation strategies. Proficient in Python and SQL, with a strong understanding of machine learning theory and statistical modeling.

Highest-signal resume keywords
  • End-To-End ML Solutions
  • Python Programming
  • SQL Proficiency
  • Machine Learning Theory
  • Experimental Design
ATS Optimization KeywordsHard Skills
  • Machine Learning
  • Data Preparation
  • Algorithm Selection
  • Feature Engineering
  • Statistical Modeling
  • A/B Testing
  • Causal Inference
  • Deep Learning
  • Problem Formulation
  • Evaluation Strategy
Soft Skills
  • Collaboration
  • Communication
Industry Keywords
  • AI-Driven Systems
  • Machine Learning Software Development Lifecycle
Tools & Technologies
  • Pandas
  • Scikit-Learn
  • PySpark
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