Machine Learning Experts in Nuremberg
matched in minutes from over 15,000 CVsHire experts who design predictive models, production-grade data pipelines and computer vision solutions with tools such as Python, PyTorch and scikit-learn. FRATCH connects you quickly with vetted, available freelancers whose skills match your project.
Meet FRATCH Experts in Nuremberg, who have recently used Machine Learning
Sabine L.
Last position:
Communications consultant for crisis communication/issue management at AQIM – Association for Quality in Interim Management
Planning, execution & moderation of crisis communication and reputation management
AQIM is a non-profit organization based in Austria. An association of interim managers from Germany and Austria. Founded in December 2024. Multi-part training with workshops and consulting for preventive crisis communication, in cooperation with the founding members and those responsible for communication. To assess crisis potential for the market entry strategy and ahead of a digital campaign.
The project in a nutshell:
Consulting and training for the purpose of:
- Analysis of crisis potential as a new market entrant and its internet-based communication activities
- Planning and developing crisis communication with targeted measures
- Developing a crisis plan to minimize reputation risks in an emergency
- Conducting training and information sessions
Success:
- What started as a one-time consulting event for preparing an information campaign became a multi-part workshop on preventive crisis communication. This not only gave the organization expertise and confidence in the event of acute challenges, but also gave the participating interim managers new skills for their day-to-day work on assignments
- The workshop enabled those responsible to launch the information campaign that was being prepared at the time and planned for six months, as scheduled at the end of January 2026 on social media
- Careful scheduling and defining how to handle critical voices in terms of terminology and tone, as well as compliance rules, ensured a smooth start and run of a 3-month information campaign
David O.
Last position:
Research Intern at Pattern Recognition Lab
- Spearheaded the integration of a custom Transformer-based encoder into the AFFGANwriting pipeline, replacing the legacy VGG19 architecture to capture richer, high-fidelity writer-style representations.
- Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
- Enhanced OCR performance by 20% by implementing a teacher-student framework that leveraged a TrOCR benchmark model for auxiliary training alignment
Amir A.
Last position:
Master's Thesis at Friedrich-Alexander University
- Analysis of the role of marriage as an informal insurance in Germany using econometric methods (supervisor: Prof. Dr. Harald Tauchmann).
- Prepared and cleaned multiple panel datasets and extracted relevant variables to create a final panel dataset with over 36,000 observations (2006–2020, SOEP).
- Designed an IV model in Stata to examine the causal link between couple separation and health shocks (mental/physical).
- Conducted robustness checks to ensure stability and validity of the results.
Partha N.
Last position:
AI Software Developer at Fraunhofer IIS
- Built a custom AI chatbot for an e-commerce client using GPT-4 and LangChain with RAG, reducing customer support ticket volume by 45% and improving response accuracy to 92%.
- Designed and deployed an intelligent document processing system using LlamaIndex, Pinecone, and FastAPI for a FinTech startup, enabling semantic search across 100K+ financial documents.
- Developed multi-agent AI workflows using CrewAI and LangGraph for a marketing agency, automating lead research, content generation, and outreach — saving 20+ hours/week of manual work.
- Created AI-powered automation pipelines using n8n, Make, and Zapier integrated with CRMs (GoHighLevel, HubSpot), reducing manual data entry by 80% for a real estate firm.
- Delivered prompt engineering and LLM fine-tuning consulting for multiple clients, optimizing AI model outputs for customer support, content creation, and data extraction use cases.
- Built production-ready REST APIs with Python and FastAPI to serve AI models on AWS and GCP, handling 10K+ daily requests with 99.9% uptime.
Arun Sai T.
Last position:
AI-Backend Developer Intern at Calvergy UA
- Integrated complex AI-based energy system models into the frontend framework, enabling the visualization of insights for 6+ key clients and maximizing energy utilization.
- Maximized energy efficiency and utilization by architecting the seamless data flow between AI models and the user interface for rapid, actionable reporting.
Muntaha S.
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
Felix K.
Last position:
Senior Data Scientist at Novartis Pharma AG
- Led the implementation and validation of digital technology for real-world mobility assessment across clinical trials with high compliance rates
- Headed a cross-functional initiative that bridged clinical and data science teams, aligning digital technology integration with strategic goals
- Innovated algorithm development for sensor data analysis, leveraging time-series data to extract novel insights and improve predictive accuracy
- Designed and deployed predictive models using machine learning, integrating digital device data with clinical datasets
- Delivered strategic insights through the creation of dashboards and reports, ensuring effective data quality control and visualization for clinical trials
- Ensured reproducibility in data science workflows by establishing robust coding practices, comprehensive documentation, and version control
- Co-led exploratory statistical plans for digital biomarkers, advancing understanding and application of digital technologies in healthcare research
- Advised clinical teams as a subject-matter expert on digital technologies, fostering strategic integration and effective utilization in clinical trials
- Managed stakeholder engagement by collaborating with internal and external stakeholders, including contract research organizations and technology vendors
- Influenced decision-making by presenting analyses and findings to various management levels and stakeholders, emphasizing actionable insights and implications
- Disseminated knowledge through peer-reviewed publications
Puranjan B.
Last position:
Internship - Generative AI at Continental
- Gathered tire images and their feature descriptions.
- Cleaned dataset of image metadata using pandas.
- Stored image feature embeddings in Chroma vector db.
- Used image augmentations to increase dataset size.
- Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
- Used PyTorch to train and test different neural networks.
- Validated model using custom accuracy metric based on similarity search in ChromaDB.
- Visualized accuracy predictions using matplotlib.
- Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
- Created custom Docker image in Amazon Elastic Container Registry for machine learning script.
Pawan S.
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Ralph N.
Last position:
AI Lead Engineer Car Configurator for leading German premium manufacturer at e-ntegration GmbH
- Intent-driven approach to configure all models across all series automotive in all distribution markets of this car manufacturer
- Developed a customer-facing, conversation-driven integration layer to achieve 100% hallucination-free technical configurations
- Utilized Microsoft Azure AI Services: AI Foundry, Agent Service, AI Search; Prompt Shield Services; Content Security; Terraform; API Gateway; AI Gateway; Container Services; Azure Agent SDK; Agent Skills; RAG; MCP Servers and tools
Uddipan B.
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Musaib P.
Last position:
Research Intern – Exploring Reasoning with Diffusion Models at Machine Learning and Perception group, FAU Erlangen-Nürnberg
- Investigating the equivalence between the Tiny Reasoning Model (TRM) and diffusion models for structured reasoning tasks such as Sudoku and maze solving.
- Exploring the reasoning and generative capabilities of diffusion models in symbolic problem-solving environments.
Jisa S.
Last position:
Electrical Design Engineer Trainee at STEP Global Corporate Solutions LLC
- Electrical system design in power distribution, lighting, and low-voltage systems
- Drafted and designed electrical layouts using AutoCAD Electrical
- Performed lighting simulation and analysis with Dialux
- Utilized Revit MEP in Electrical for building information modeling
- Conducted electrical estimation and takeoff using PlanSwift
- Estimated material and labor costs for electrical projects
Ashmi J.
Last position:
Software Developer at Myrix Labs
- Engineered high-performance APIs with FastAPI + MongoDB, integrating live weather data (NOAA, NWS).
- Developed an AI chatbot with OpenAI APIs — context-aware by location, profession & interests.
- Created admin dashboard APIs for real-time monitoring and zero-downtime configuration.
- Integrated Stripe Embedded Payments with secure transactions & subscription management via webhooks.
Usman S.
Last position:
Working Student – Research Assistant at NSQUARED Lab, Friedrich-Alexander University
- Design and implementation of the DEXTER project, a tendon-driven robotic hand mimicking human hand biomechanics
- Design and 3D printing of the hand along with associated control algorithms and circuitry
Discover over 15,000 top freelancers
Statistics of experts using Machine Learning
Aggregated from the professional profiles of matched freelancers.
Experience
11 years (Germany: 14 years)
Position duration
1.8 years (Germany: 2.8 years)
Positions per freelancer
7 (Germany: 9)
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Research and Development, Business Intelligence
Bachelor's degree or higher
100% (Germany: 97%)
Master's degree or higher
81% (Germany: 77%)
Doctorate
5% (Germany: 18%)
Certifications per freelancer
2
Most common languages
English, German, Hindi
Speak two or more languages
100% (Germany: 98%)
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Nuremberg are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Nuremberg using Machine Learning
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Machine Learning experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (86%)
- Education (52%)
- Automotive (48%)
- Manufacturing (48%)
- Banking and Finance (29%)
- Professional Services (29%)
- Energy (24%)
- Healthcare (24%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Machine Learning covers
Machine Learning enables software to identify patterns in data and use them to produce predictions, classifications or recommendations. Experts apply supervised, unsupervised and reinforcement learning to business problems such as demand forecasting, fraud detection, search, personalisation and industrial quality control. The work spans experimentation, model design and reliable operation in production.
Typical applications
Machine Learning appears in products and internal systems that need data-driven decisions rather than fixed rules.
- Predictive maintenance for connected equipment
- Document, image and speech classification
- Recommendation and ranking systems
- Forecasting for supply chains and operations
- Anomaly detection and risk assessment
Ecosystem and tooling
Python is the dominant language for model development, with pandas and NumPy supporting data preparation and PyTorch, TensorFlow or scikit-learn supporting training. Strong specialists also work with notebooks, experiment tracking, feature stores and model registries. Cloud services, containerisation and orchestration help teams deploy models as dependable APIs or batch workloads.
When companies need expertise
Companies often bring in freelance specialists when a promising proof of concept must become a tested product, when internal data is difficult to use, or when model performance has stopped improving. They may also need focused support for data pipelines, model monitoring, retraining workflows or a responsible transition from manual rules to predictive systems. In Nuremberg, collaboration may combine remote delivery with on-site workshops and German or English communication.
What strong professionals deliver
Good Machine Learning professionals connect technical decisions to measurable business outcomes. They inspect data quality before selecting an algorithm, establish sound validation methods and explain trade-offs to stakeholders. Their deliverables can include prepared datasets, reproducible training pipelines, documented models, evaluation reports, deployment interfaces and monitoring plans. They also consider privacy, bias, interpretability and operational cost from the start.
Skills beside modelling
Model training is only one part of a successful implementation. The right specialist understands software engineering, statistics, data architecture and the operational environment in which predictions will run.
- Build maintainable Python services and data workflows
- Design APIs, batch jobs and integration patterns
- Track experiments, versions and model behaviour
- Prepare cloud or on-premise deployment processes
- Translate business questions into testable objectives
Frequently asked questions
What clients ask us most about Machine Learning — answered in short.
Machine Learning is used to find useful patterns in data and turn them into predictions, classifications, recommendations or automated decisions. Common applications include demand forecasting, fraud detection, image analysis, natural language processing and predictive maintenance.
Machine Learning learns relationships from examples, while rule-based software follows logic written directly by people. Learning-based systems can handle complex or changing patterns, but they require suitable data, careful validation and ongoing monitoring.
A strong Machine Learning specialist usually combines Python, statistics, data preparation and model evaluation with software engineering practices. Experience with SQL, cloud infrastructure, APIs, Docker, experiment tracking and deployment is valuable when a model must run reliably in production.
The required background depends on the work. A focused proof of concept may need a specialist who can select data, establish a baseline and test models, while a production system calls for experience with deployment, monitoring, security, reproducibility and stakeholder communication.
Machine Learning work is often well suited to remote collaboration because data exploration, coding and model reviews happen in shared environments. On-site sessions in Nuremberg can still help with access requirements, domain workshops and alignment with local product or operations teams.
Ask how the specialist handled data quality, leakage, validation and changing production data in a relevant project. Review concrete deliverables such as evaluation reports, reproducible pipelines and monitoring plans rather than judging a model only by its headline result.
Machine Learning is a major area within artificial intelligence in which systems learn from data. Deep learning is a specialised approach that uses neural networks with many layers, while artificial intelligence also includes methods that do not learn from examples.
Common Machine Learning tools include Python, NumPy, pandas, scikit-learn, PyTorch and TensorFlow. Teams may also use SQL, notebooks, experiment tracking, model registries, cloud services and container platforms to move work from exploration into dependable operation.
The average hourly rate of freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects is 59 €, which corresponds to a daily rate of about 469 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects, 100% hold at least a Bachelor's degree, 81% hold at least a Master's degree, and 5% hold a doctorate.
On average, freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects have 11 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects are English (100%), German (95%), and Hindi (24%).
The most common industries among freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects are Information Technology (86%), Education (52%), and Automotive (48%).
The most common business areas among freelancers in Nuremberg, Germany who have used Machine Learning in their recent projects are Research and Development (86%), Information Technology (81%), and Product Development (71%).
Main locations of FRATCH Experts, who have recently used Machine Learning
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
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Stuttgart 
Dusseldorf 
Dortmund 
Essen 
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Dresden 
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FRATCH CEO
