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Internship German Machine Learning information

Which 3 jobs will survive AI?

Jobs that require complex human skills such as machine learning engineers, data scientists, and cybersecurity specialists are likely to persist as AI automates routine tasks. These roles demand critical thinking, creativity, and specialized knowledge that are difficult for AI to replicate fully. Continuous learning and expertise in AI tools can enhance job security in these fields.

What is an Internship German Machine Learning?

An Internship German Machine Learning is a temporary training position typically offered by companies or research institutions in Germany, focusing on practical experience in machine learning. Interns work on real-world projects involving data analysis, algorithm development, and model implementation under supervision. These internships help students or recent graduates gain hands-on skills, industry exposure, and networking opportunities in the rapidly growing field of artificial intelligence and machine learning.

What is the difference between Internship German Machine Learning vs Data Scientist German?

AspectInternship German Machine LearningData Scientist German
Required CredentialsBasic programming, coursework in MLAdvanced degree in data science, statistics, or related
Work EnvironmentInternship setting, learning-focusedFull-time, project-driven
Industry UsageEntry-level roles, training programsProfessional roles, decision-making

Internship German Machine Learning positions are typically entry-level, focusing on learning and skill development, often requiring basic programming and coursework. Data Scientist German roles are more advanced, requiring higher education and experience, with responsibilities in analyzing data and building models. The internship provides a stepping stone into the data science field, while the data scientist role involves applying expertise to solve complex problems.

Can foreigners intern in Germany?

Foreigners can intern in Germany if they meet visa and work authorization requirements, which vary depending on their nationality and the internship duration. Internships often require a valid visa or residence permit, especially for non-EU/EEA citizens, and may need to comply with labor laws and internship regulations. It is important to check specific visa conditions and employer sponsorship options before applying.

How much do ML interns get paid?

Machine Learning interns typically earn between $15 and $30 per hour, depending on the company, location, and level of experience. Paid internships often include opportunities to work with tools like Python, TensorFlow, or PyTorch and may be full-time or part-time during the summer or semester.

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

As a German Machine Learning intern, you'll typically assist with real-world projects such as developing and testing machine learning models, preprocessing datasets, and supporting the implementation of AI solutions in both German and international contexts. You may also help with data analysis, model evaluation, and documentation, often collaborating with data scientists and engineers. These projects provide hands-on experience with industry-standard tools and workflows, helping you build practical skills and a strong professional network.

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

To thrive as an Internship German Machine Learning, you need a solid understanding of machine learning concepts, programming skills in Python, and progress toward a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, and data analysis libraries, as well as experience using version control systems like Git, is typically required. Strong analytical thinking, problem-solving ability, and effective communication—especially in both English and German—help you collaborate within diverse teams. These skills and qualifications are essential for successfully contributing to machine learning projects and adapting to the fast-evolving tech industry.

Is AI in demand?

AI skills are highly in demand for machine learning internships, including roles focused on developing and applying artificial intelligence technologies. Companies seek candidates with knowledge of programming languages like Python, experience with frameworks such as TensorFlow or PyTorch, and understanding of data analysis. The AI industry continues to grow, creating numerous opportunities for interns with relevant skills and training.
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Internship German Machine Learning jobs near you
Research Engineer - Machine Learning & Robotics

Research Engineer - Machine Learning & Robotics

Jumio

Lenexa, KS

Other

Posted 15 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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