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Internship Machine Learning information

See Kansas salary details

$22.7K

$38K

$78.5K

How much do internship machine learning jobs pay per year?

As of Jul 8, 2026, the average yearly pay for internship machine learning in Kansas is $37,978.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,000.00 and $41,000.00 per year, depending on experience, location, and employer.

What is the difference between Internship Machine Learning vs Data Science Intern?

AspectInternship Machine LearningData Science Intern
Required CredentialsBasic programming, introductory ML knowledgeStatistics, programming, data analysis basics
Work EnvironmentHands-on ML model development, codingData analysis, visualization, reporting
Industry UsageTech, AI companies, research labsBusiness, finance, healthcare sectors

Internship Machine Learning focuses on developing and implementing machine learning models, requiring programming and ML fundamentals. Data Science Internships involve analyzing data, creating reports, and supporting decision-making. Both roles are common in tech and research industries, but ML internships are more specialized in model building, while Data Science internships emphasize data analysis and visualization.

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

To thrive as a Machine Learning Intern, you generally need a solid grounding in mathematics, programming (especially Python), and familiarity with machine learning concepts, often supported by coursework or relevant projects. Experience with tools and libraries like TensorFlow, scikit-learn, and Jupyter Notebooks, as well as knowledge of version control systems like Git, is typically expected. Strong problem-solving skills, willingness to learn, and effective communication set outstanding interns apart. These skills and qualities enable interns to contribute meaningfully to projects, adapt quickly, and collaborate well within technical teams.

What are internship machine learning positions?

Internship machine learning positions are temporary roles for students or recent graduates to gain hands-on experience in the field of machine learning. Interns typically work on real-world projects involving data analysis, model development, and algorithm implementation under the guidance of experienced professionals. These internships provide valuable exposure to machine learning tools, programming languages such as Python, and industry best practices. They are an excellent way to build technical skills, enhance your resume, and explore career opportunities in artificial intelligence and data science.

What types of projects can I expect to work on during a Machine Learning internship?

As a Machine Learning intern, you may work on a variety of projects such as data preprocessing and cleaning, developing and testing machine learning models, or assisting with research experiments. These projects often involve collaborating closely with data scientists and engineers, learning to use popular frameworks like TensorFlow or PyTorch, and presenting your findings to the team. The scope and complexity of your assignments will typically grow as you demonstrate proficiency and initiative, providing valuable real-world experience and networking opportunities.
What are the most commonly searched types of Machine Learning jobs in Kansas? The most popular types of Machine Learning jobs in Kansas are:
What are popular job titles related to Internship Machine Learning jobs in Kansas? For Internship Machine Learning jobs in Kansas, the most frequently searched job titles are:
What job categories do people searching Internship Machine Learning jobs in Kansas look for? The top searched job categories for Internship Machine Learning jobs in Kansas are:
Internship Machine Learning jobs near you
Infographic showing various Internship Machine Learning job openings in Kansas as of July 2026, with employment types broken down into 7% Internship, 1% As Needed, 74% Full Time, 16% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% Remote job distribution, with an average salary of $37,978 per year, or $18.3 per hour.
Research Engineer - Machine Learning & Robotics

Research Engineer - Machine Learning & Robotics

Jumio

Lenexa, KS

Other

Posted 12 days ago

Job description

Role Purpose

Jumio is looking for a Research Engineer with a foundation in machine learning, robotics, and data infrastructure to help build and scale the systems used for data collection, model development, and product improvement.

This role sits at the intersection of robotics, computer vision, and applied machine learning. You will work hands-on with robotic systems, ROS/ROS2-based modules, mobile data collection workflows, and ML pipelines that support training, evaluation, and production model performance. This is a strong opportunity for a new graduate or early-career engineer who wants to build practical systems that directly improve real-world AI products.

Role Value

High-quality data and reliable model evaluation infrastructure are critical to improving Jumio's machine learning and computer vision capabilities. This role helps ensure that data collected from robotic systems and mobile applications is usable, scalable, and connected to the broader model development lifecycle.

The Research Engineer will support both the robotics/data collection environment and the ML development workflow, helping the team move faster, improve model quality, and better understand model performance in production.

Example Responsibilities
  • Build and integrate ROS/ROS2-based modules to support robotic navigation, manipulation, and data collection workflows.
  • Replicate and integrate mobile and web UI environments into robotic testing and data collection systems.
  • Build, maintain, and improve training and test datasets collected through robotic manipulators and in-house iOS and Android applications.
  • Mine, query, and analyze data from internal databases to create features, identify trends, and generate insights that improve product and model development.
  • Develop tools and processes to monitor data quality, model performance, and model accuracy in production environments.
  • Implement end-to-end machine learning workflows, including data preparation, model training, testing, evaluation, and deployment support.
  • Write clean, modular, well-documented C++ and Python code that can be maintained and extended by other engineers.
  • Collaborate cross-functionally with machine learning, engineering, product, and research teams to improve data collection, model development, and system performance.
Required Experience
  • Bachelor's or Master's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field.
  • 1-2 years of relevant industry, internship, or research experience in machine learning, robotics, computer vision, or related technical areas.
  • Hands-on experience with ROS and/or ROS2, including building or integrating modules for robot navigation, manipulation, simulation, or data collection.
  • Strong foundation in machine learning fundamentals, with experience implementing models in Python using frameworks such as PyTorch, TensorFlow, scikit-learn, or similar.
  • Experience working with databases, writing queries, and building or maintaining data pipelines for training, testing, or evaluation.
  • Strong programming skills in Python and C++, with an emphasis on clean, reliable, well-documented code.
  • Ability to work hands-on with physical hardware, debug system behavior, and translate research or prototype work into scalable engineering solutions.
Nice to Have
  • Experience with robotic manipulators, mobile robot platforms, or lab-based robotic systems.
  • Familiarity with iOS and/or Android development, especially for hardware-integrated data collection applications.
  • Experience with data collection pipelines for computer vision, biometric systems, identity verification, or similar applied AI domains.
  • Exposure to production ML observability, model monitoring, drift detection, or data quality monitoring tools.
  • Familiarity with cloud platforms such as AWS, including S3, EC2, SageMaker, or similar tools for storage, compute, and model deployment.
  • Experience working in cross-functional environments with machine learning engineers, software engineers, researchers, and product teams.

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