Werkstudent Prototyping - smart labeling (m/w/d)

📍 Job Overview

Job Title: Werkstudent Prototyping - smart labeling (m/w/d)

Company: isento GmbH

Location: Nuremberg, Bavaria, Germany

Job Type: INTERN

Category: Engineering / Data Science / AI Operations

Date Posted: September 07, 2026

Experience Level: Entry-Level (0-2 years)

Remote Status: Hybrid

🚀 Role Summary

• This internship focuses on the hands-on development of a prototype for smart labeling, contributing to innovative AI and Machine Learning projects.

• The role involves building an intuitive web interface and backend, directly impacting the user experience and functionality of the smart labeling solution.

• You will integrate existing data pipelines and leverage advanced techniques in object detection and image segmentation, crucial for modern AI operations.

• This position offers a unique opportunity to gain practical experience in a research-oriented project within a recognized Top Employer, fostering personal and professional growth.

📝 Enhancement Note: This role is classified under Engineering/Data Science/AI Operations due to its focus on prototyping AI/ML solutions, involving data pipelines, object detection, and image segmentation. The "Werkstudent" title in Germany typically refers to a student working part-time alongside their studies, often with a focus on practical application of academic knowledge. The "m/w/d" designation (männlich/weiblich/divers) signifies that the position is open to all genders.

📈 Primary Responsibilities

• Prototype Development: Further develop an existing prototype to effectively demonstrate various use cases for smart labeling technology.

• Web Interface & Backend Engineering: Design and build an intuitive, user-friendly web interface and the corresponding backend system to support the smart labeling functionality.

• Data Pipeline Integration: Connect and integrate existing training and data pipelines to ensure seamless data flow and model training for the prototype.

• AI/ML Implementation: Integrate advanced procedures for object detection and image segmentation, leveraging cutting-edge techniques in computer vision.

• Codebase Familiarization: Efficiently onboard and contribute to an existing codebase, demonstrating adaptability and strong problem-solving skills within a dynamic development environment.

📝 Enhancement Note: The responsibilities are tailored to reflect a typical intern's contribution to a prototype development project in an AI/ML context. Emphasizing integration and development within existing frameworks suggests a need for strong foundational programming skills and the ability to learn and adapt quickly, rather than leading architecture design.

🎓 Skills & Qualifications

Education: Currently pursuing a degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Electrical Engineering, or a closely related technical field. A Bachelor's degree is the minimum academic requirement.

Experience: 0-2 years of practical experience, preferably gained through academic projects, internships, or personal development.

Required Skills:

• Python Proficiency: Strong command of Python, essential for backend development, data manipulation, and ML model integration.

• PyTorch Expertise: Solid understanding and practical experience with PyTorch for developing and implementing machine learning models.

• Machine Learning/Deep Learning Fundamentals: Demonstrated practical experience in ML/DL concepts, including model training, evaluation, and deployment considerations.

• Data Pipeline Understanding: Familiarity with data pipelines, including their design, implementation, and maintenance for ML workflows.

• Image Processing: Practical experience with image processing techniques and libraries.

• Self-Directed Work Ethic: Ability to work independently, structure tasks effectively, and a proactive approach to problem-solving and experimentation.

• Language Proficiency: German language skills at a minimum B2 level for effective team and client communication.

Preferred Skills:

• BigQuery: Experience with Google BigQuery for data warehousing and analysis.

• Google Cloud Storage (GCS): Familiarity with GCS for cloud-based data storage.

• Optuna: Experience with Optuna for hyperparameter optimization.

• AutoML: Understanding or experience with Automated Machine Learning tools.

• OpenCV: Practical knowledge of OpenCV for computer vision tasks.

• PIL (Pillow): Experience with the Python Imaging Library for image manipulation.

• Grounding-DINO: Familiarity with advanced object detection models like Grounding-DINO.

• COCO Dataset: Understanding of common object detection datasets like COCO.

• REST APIs: Initial experience with developing or consuming RESTful APIs.

• Web UIs: Basic understanding or experience with web user interface development.

📝 Enhancement Note: The preferred skills list includes specific tools and libraries that are highly relevant to modern AI/ML development, particularly in computer vision and cloud environments. Highlighting these indicates the company's tech stack and the specific areas where candidates can demonstrate advanced knowledge. The inclusion of specific models like Grounding-DINO and datasets like COCO points towards a focus on state-of-the-art computer vision applications.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Project Showcase: Include academic or personal projects demonstrating practical application of Python, PyTorch, and ML/DL principles, ideally related to image processing or object detection.

• Code Repository: Provide links to publicly accessible code repositories (e.g., GitHub) showcasing your coding style, project structure, and contributions.

• Technical Documentation: For key projects, include brief documentation explaining the problem statement, methodology, implemented solutions, and achieved results.

• Process Flow Examples: If possible, illustrate a simple data pipeline or ML workflow you have implemented, highlighting data ingestion, processing, and model interaction.

Process Documentation:

• Workflow Design: Demonstrate understanding of how to break down complex problems into manageable development steps for prototype creation.

• Integration Strategy: Show examples of how you've integrated different software components or libraries to achieve a functional outcome.

• Performance Analysis: If applicable, include examples of how you've evaluated the performance of your models or developed applications, even if basic.

📝 Enhancement Note: For an intern role, the portfolio requirements are focused on demonstrating foundational skills and a proactive learning attitude rather than extensive professional experience. The emphasis is on tangible evidence of coding ability, understanding of ML/DL concepts, and the ability to work with existing systems and data.

💵 Compensation & Benefits

Salary Range: As an intern position (Werkstudent) in Nuremberg, Germany, a typical gross monthly salary range would be between €12 - €16 per hour, translating to approximately €960 - €1,280 per month for a standard 20-hour work week. This range is based on current market data for student internships in the IT and engineering sectors in major German cities, considering the cost of living and typical compensation for entry-level roles.

Benefits:

• Flexible Working Hours: Adapt your work schedule to accommodate your academic commitments, promoting a healthy work-life balance.

• Hybrid Work Model: Enjoy the flexibility of combining remote work with in-office collaboration, fostering productivity and personal well-being.

• Team Events: Participate in regular company events such as BBQs, game nights, "Duke Days," and summer parties, fostering team cohesion and a positive social environment.

• Individual Growth & Development: Benefit from personalized training and development opportunities, supporting your continuous learning and career advancement.

• Supportive Work Environment: Experience a friendly and respectful workplace culture, recognized by multiple Top Employer awards, where collaboration and mutual support are paramount.

Working Hours: This is a part-time internship position. The working hours are flexible and will be arranged in agreement with your team, typically around 20 hours per week to allow for academic study.

📝 Enhancement Note: The salary estimate is based on typical "Werkstudent" compensation rates in Germany for technical internships. The benefits are directly extracted from the provided text and contextualized for an intern role, emphasizing development and work-life balance.

🎯 Team & Company Context

🏢 Company Culture

Industry: Information Technology (IT) Consulting and Project Realization. isento GmbH specializes in delivering IT projects from conception to rollout for their clients.

Company Size: 51-200 employees. This mid-size range suggests a company that is established enough to handle significant projects but still retains a close-knit, agile culture where individual contributions are highly visible.

Founded: The founding date is not specified, but the company's multiple years of operation and consistent recognition as a Top Employer indicate a stable and reputable organization.

Team Structure:

• Cross-functional Teams: The company operates with teams that likely consist of various IT specialists (developers, project managers, consultants) collaborating on client projects.

• Project-Based Collaboration: Team dynamics are heavily influenced by project needs, requiring adaptability and strong communication skills to integrate into different project teams.

• Mentorship: As a Top Employer with a focus on growth, there's an implied structure that supports mentorship and knowledge transfer between more experienced team members and interns.

Methodology:

• Agile Project Management: While not explicitly stated, the focus on concept-to-rollout delivery and client solutions suggests an agile or iterative approach to project management.

• Client-Centric Solutions: The core methodology revolves around delivering the best solutions for clients, implying a focus on understanding client needs and translating them into technical outcomes.

• Team-Oriented Development: The company emphasizes a "team effort" approach, indicating collaborative development processes and shared responsibility for project success.

Company Website: http://isento.de

📝 Enhancement Note: The company culture is characterized by a strong team ethos, a focus on client success, and a commitment to employee growth, as evidenced by their Top Employer awards. For an intern, this means opportunities for learning within a supportive yet dynamic project environment.

📈 Career & Growth Analysis

Operations Career Level: This role is an entry-level internship position within the broader field of AI/ML and Software Development. It serves as an excellent stepping stone for students seeking practical experience in areas like AI prototyping, data pipeline management, and full-stack development.

Reporting Structure: The intern will likely report to a project lead or a senior engineer who will provide guidance and mentorship. They will be part of a project team, collaborating with peers and senior members.

Operations Impact: While an intern's direct impact is typically on the project at hand, success in this role contributes to the development of innovative prototypes that can influence future client solutions and internal R&D efforts in AI and Machine Learning. Demonstrating strong performance can lead to future opportunities within isento.

Growth Opportunities:

• Skill Specialization: Opportunity to deepen expertise in Python, PyTorch, and specific ML/DL libraries (e.g., OpenCV, Grounding-DINO) through hands-on project work.

• Cross-Functional Exposure: Gain insight into different facets of IT project delivery, from backend development and UI design to data pipeline integration and AI model implementation.

• Professional Development: Access to individual training and development programs offered by isento, tailored to personal career goals and company needs.

• Potential for Future Employment: Strong performance and a good cultural fit can open doors for future internship opportunities or even full-time positions upon graduation.

📝 Enhancement Note: The growth analysis focuses on the developmental aspects of an internship, highlighting skill acquisition, practical experience, and the potential for future career advancement within the company, aligning with isento's stated commitment to employee growth.

🌐 Work Environment

Office Type: The company offers a hybrid work model, indicating a blend of in-office and remote work. This suggests a modern approach to workplace flexibility.

Office Location(s): The primary office is located in Nuremberg, Bavaria, Germany. This location is central to the company's operations and client interactions in the region.

Workspace Context:

• Collaborative Spaces: The office environment likely includes collaborative spaces designed for teamwork, brainstorming, and client meetings, facilitating interaction among team members.

• Modern Tech Infrastructure: As an IT company, expect access to up-to-date hardware, software, and development tools necessary for efficient prototyping and development.

• Team Interaction: The hybrid model allows for both focused independent work (remote) and direct team collaboration and knowledge sharing (in-office), fostering a dynamic work atmosphere.

Work Schedule: Flexible working hours are a key benefit, allowing interns to balance their academic responsibilities with project work. This typically means setting a schedule that aligns with study commitments and team availability, often around 20 hours per week.

📝 Enhancement Note: The hybrid work environment and flexible hours are key features for attracting students and ensuring a good work-life balance, which is crucial for a "Werkstudent" role.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your application, CV, and any provided portfolio materials to assess qualifications and suitability.

• Technical Interview(s): Expect discussions focused on your technical skills in Python, PyTorch, ML/DL concepts, and image processing. You may be asked to walk through past projects or solve hypothetical coding problems.

• Team/Cultural Fit Interview: An opportunity to discuss your motivation, work style, and how you align with isento's team-oriented culture. This is where you can highlight your self-directed and experimental approach.

• Offer: If successful, you will receive an offer for the internship position.

Portfolio Review Tips:

• Curate Effectively: Select 2-3 of your most relevant projects that best showcase your Python, PyTorch, and ML/DL skills, especially those involving image processing or data pipelines.

• Highlight Your Role: Clearly articulate your specific contributions to each project, especially if it was a team effort. Use "I" statements for your individual work.

• Demonstrate Problem-Solving: For each project, explain the challenge, your approach, the tools/libraries used (e.g., PyTorch, OpenCV), and the outcome or lessons learned.

• Code Clarity: Ensure your code repositories are clean, well-commented, and include a README file explaining how to set up and run your projects.

• Showcase Adaptability: If you've worked with different libraries or integrated systems, highlight this to demonstrate your ability to learn and adapt.

Challenge Preparation:

• Coding Fundamentals: Brush up on Python fundamentals, data structures, and algorithms.

• PyTorch Basics: Be prepared to discuss basic PyTorch concepts, model building, and training loops.

• ML/DL Concepts: Review core ML/DL principles, including supervised learning, image segmentation, and object detection.

• Problem-Solving Scenarios: Think about how you would approach a new problem, break it down, and select appropriate tools. Be ready to articulate your thought process.

• Company Research: Understand isento's focus on IT projects, client solutions, and their AI/ML initiatives.

📝 Enhancement Note: The interview and portfolio advice is tailored for an intern role, focusing on demonstrating foundational technical skills, a proactive learning attitude, and cultural fit rather than extensive professional experience.

🛠 Tools & Technology Stack

Primary Tools:

• Python: The core programming language for development, data analysis, and ML model implementation.

• PyTorch: The primary deep learning framework for building and training models.

• Version Control (e.g., Git): Essential for collaborative development and code management.

Analytics & Reporting:

• BigQuery (Preferred): For large-scale data warehousing and analytical queries.

• Data Visualization Tools (Implicit): Tools to present insights from data analysis, potentially integrated into the web UI.

CRM & Automation:

• REST APIs: For communication between the web UI, backend, and potentially other services.

• Cloud Platforms (e.g., Google Cloud Storage - GCS): For data storage and potentially model deployment.

• Automation Libraries (e.g., Optuna): For optimizing model hyperparameters.

Computer Vision Libraries:

• OpenCV (Preferred): For a wide range of computer vision tasks, including image processing and object detection.

• PIL (Pillow) (Preferred): For basic image manipulation and format handling.

• Specific Models (e.g., Grounding-DINO): For advanced object detection capabilities.

📝 Enhancement Note: The technology stack is heavily focused on AI/ML development, with a strong emphasis on Python, PyTorch, and relevant computer vision libraries. Preferred tools like BigQuery and GCS suggest a cloud-native development environment.

👥 Team Culture & Values

Operations Values:

• Team Spirit: A strong emphasis on collaboration, mutual support, and relying on each other to achieve common goals. Expect to be part of a supportive team environment.

• Customer Focus: Dedication to delivering the best possible solutions for clients, requiring an understanding of project requirements and a commitment to quality.

• Innovation & Experimentation: Encouragement to try new things, experiment with solutions, and find creative ways to overcome challenges, particularly relevant in the R&D aspect of this role.

• Personal Growth: A commitment to individual development through training and providing opportunities for employees to expand their skills and careers.

• Respect and Friendliness: A core value reflected in their "Top Arbeitgeber" status, ensuring a positive and inclusive workplace atmosphere.

Collaboration Style:

• Proactive Communication: Open communication channels are essential, especially in a hybrid environment, to ensure alignment and efficient progress on projects.

• Shared Responsibility: Team members are expected to contribute actively and take ownership of their tasks, working together towards project success.

• Knowledge Sharing: Opportunities to learn from and share knowledge with colleagues, fostering a continuous learning environment.

📝 Enhancement Note: The company culture strongly emphasizes teamwork, client success, and personal development, creating an environment conducive to learning and growth for interns.

⚡ Challenges & Growth Opportunities

Challenges:

• Onboarding to Existing Codebase: Integrating into and understanding a pre-existing codebase requires strong analytical skills and the ability to quickly grasp complex logic.

• Rapid Prototyping: The nature of prototype development often involves fast iteration cycles and adapting to evolving requirements or technical discoveries.

• Balancing Study and Work: Effectively managing time between academic commitments and internship responsibilities to meet project deadlines.

• Technical Complexity: Working with advanced AI/ML concepts like object detection and image segmentation can be technically challenging and requires continuous learning.

Learning & Development Opportunities:

• Practical AI/ML Application: Gain hands-on experience applying theoretical knowledge of Python, PyTorch, and ML/DL to real-world prototyping challenges.

• Industry Exposure: Work on cutting-edge projects in areas like smart labeling, robotics, and AI, providing valuable insight into current industry trends.

• Professional Skills Development: Enhance skills in areas such as problem-solving, communication, teamwork, and time management within a professional setting.

• Mentorship and Guidance: Receive support and guidance from experienced professionals, aiding in skill development and career exploration.

📝 Enhancement Note: The challenges are framed as opportunities for growth, emphasizing the learning potential inherent in the role and the company's supportive culture for development.

💡 Interview Preparation

Strategy Questions:

• "Tell me about a challenging project you worked on in Python or PyTorch. What was the challenge, how did you approach it, and what was the outcome?" - Prepare to discuss a specific project from your academic or personal work, highlighting your problem-solving process, technical choices, and results. Focus on your role and contributions.

• "How would you approach integrating a new object detection model into an existing data pipeline?" - Think about the steps involved: understanding the pipeline, data format compatibility, model input/output requirements, testing, and potential performance implications.

• "Describe your experience with image processing or computer vision. What libraries have you used, and for what purpose?" - Be ready to discuss your practical experience with libraries like OpenCV, PIL, or specific ML models, and the types of tasks you've performed. Company & Culture Questions:

• "Why are you interested in working at isento, and what do you know about our company?" - Research isento's website, their projects, and their "Top Arbeitgeber" recognition. Connect your interests to their work in IT project realization and AI/ML.

• "How do you handle working independently on tasks, and how do you seek help when you need it?" - Emphasize your self-starter attitude while also demonstrating your willingness to collaborate and ask questions to ensure project success.

• "What are your career aspirations after graduation, and how does this internship fit into those plans?" - Show how this internship aligns with your academic and professional development goals, particularly in AI/ML or software engineering. Portfolio Presentation Strategy:

• Concise Overview: Briefly introduce each project, stating its purpose and your primary role.

• Technical Deep Dive: For 1-2 key projects, walk through the technical implementation, highlighting your use of Python, PyTorch, and any relevant libraries. Explain your design choices.

• Showcase Problem-Solving: Clearly articulate any challenges you faced and how you overcame them.

• Demonstrate Results: Present any quantifiable results or key learnings from your projects.

• Interactive Elements: If your projects have a UI or demo, be prepared to show it live or via screenshots/videos.

📝 Enhancement Note: The interview questions and portfolio presentation strategies are designed to help an intern showcase their potential, practical skills, and cultural fit, even with limited professional experience.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided application link on join.com.

• Tailor your Resume: Highlight your academic achievements, relevant coursework (Computer Science, Data Science, AI/ML), and any personal or academic projects that demonstrate your Python, PyTorch, and ML/DL skills. Use keywords from the job description.

• Prepare Your Portfolio: Gather links to your GitHub repositories showcasing relevant projects. Ensure your code is clean, well-documented, and includes a README. Select 1-2 projects to be ready to discuss in detail during an interview.

• Practice Technical Explanations: Be ready to discuss your projects, explain your technical approach, and articulate your understanding of ML/DL concepts, Python, and PyTorch. Practice explaining complex topics clearly and concisely.

• Research isento: Understand the company's mission, values, and recent projects. Prepare to articulate why you are a good fit for their team-oriented and client-focused culture.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.