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Apprentice Machine Learning Testing Jobs in Arlington, TX

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC ... Integrate with existing model serving frameworks to support testing, deployment, and rollback ...

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC ... Integrate with existing model serving frameworks to support testing, deployment, and rollback ...

System One

Machine Learning Operations Engineer

Dallas, TX · On-site

$68K - $93K/yr

Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC ... Integrate with existing model serving frameworks to support testing, deployment, and rollback ...

Equinix

AI/Machine Learning Engineer

Dallas, TX · Remote

Continuously improve system performance through testing, monitoring, and iterative refinement ... Bachelor's degree in Computer Science, Machine Learning, Data Science, or a related field The ...

Equinix, Inc.

AI/Machine Learning Engineer

Dallas, TX · On-site

$144 - $196/hr

Continuously improve system performance through testing, monitoring, and iterative refinement ... Bachelor's degree in Computer Science, Machine Learning, Data Science, or a related field Pay Range ...

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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 8, 2026, the average hourly pay for apprentice machine learning testing in Arlington, TX is $17.42, according to ZipRecruiter salary data. Most workers in this role earn between $14.71 and $19.04 per hour, depending on experience, location, and employer.

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 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 Arlington, TX? For Apprentice Machine Learning Testing jobs in Arlington, TX, the most frequently searched job titles are:
What job categories do people searching Apprentice Machine Learning Testing jobs in Arlington, TX look for? The top searched job categories for Apprentice Machine Learning Testing jobs in Arlington, TX are:
What cities near Arlington, TX are hiring for Apprentice Machine Learning Testing jobs? Cities near Arlington, TX with the most Apprentice Machine Learning Testing job openings:
Apprentice Machine Learning Testing jobs near you

Machine Learning Operations Engineer

System One

Dallas, TX • On-site

$68K - $93K/yr

Contractor

Posted 29 days ago

Job description

Job Title: Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship)

Responsibilities

  • Optimize and maintain large-scale feature engineering pipelines using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure.
  • Refactor and modularize ML codebases to enhance reusability, maintainability, and performance.
  • Collaborate with platform teams on compute capacity planning, resource allocation, and system upgrades.
  • Integrate with existing model serving frameworks to support testing, deployment, and rollback processes.
  • Monitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency.
  • Contribute to internal ML platforms by sharing insights, proposing improvements, and documenting best practices.
  • Build near real-time ML pipelines using Kafka and Spark Streaming.
  • Work with AWS and SageMaker MLOps ecosystem.
Requirements
  • 6+ years of experience in software engineering, data engineering, or MLOps roles.
  • Strong programming expertise in Python, with hands-on experience in Pandas, PySpark, and PyArrow.
  • Deep understanding of the Hadoop ecosystem, distributed computing, and performance tuning.
  • Experience with CI/CD pipelines and best practices in ML environments.
  • Hands-on experience with monitoring tools for ML pipeline health and performance.
  • Strong collaboration skills with experience working in cross-functional teams (platform, data science, engineering).
  • Experience contributing to or building internal MLOps frameworks/platforms.
  • Familiarity with SLURM clusters or other distributed job schedulers.
  • Exposure to Kafka, Spark Streaming, or other real-time data processing technologies.
  • Understanding of ML lifecycle management, including versioning, deployment, and drift detection.

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