Convolutional Neural Network Experts in Munich
for production-ready computer vision, matched in minutes with vetted freelancersHire experts who build image classification, object detection and visual inspection systems with PyTorch, TensorFlow and modern model deployment tools. FRATCH connects you with precisely matched, vetted and available freelancers quickly.
Meet FRATCH Experts in Munich, who have recently used Convolutional Neural Network
Robert D.
Last position:
Co-Founder and Managing Director at Infinite Mind GmbH
I help leadership teams turn the potential of AI into measurable business results — fast, pragmatic, and with people at the core.
As Co-Founder of Infinite Mind, I work with CEOs and innovation leaders to identify high-impact AI opportunities, design actionable solutions, and support adoption across the organization. Our focus: driving productivity gains, smarter workflows, and scalable value.
Over the past ten years, I've worked at the intersection of Digital Transformation, Data, and Machine Learning, advising companies in software, high-tech, media, and insurance. I’ve led large-scale initiatives, including the group-wide adoption of Generative AI, and understand the strategic and human challenges of driving change at scale.
I combine a technical background in machine learning (M.Sc. Electrical & Computer Engineering, TUM) with a broader perspective shaped by degrees in Physics and Philosophy (LMU Munich). In addition to my consulting work, I’ve co-founded a tech-enabled charity and supported early-stage founders as a business coach.
If you're looking to go beyond the AI hype and make it actually work in your business — let’s talk.
Stephan B.
Last position:
Freelance Data Scientist at Baier Data & AI Consulting
Martin R.
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
René W.
Last position:
Conference Operator at Brähler Systems GmbH
- Developed the iOS/Android Delegate App and the Conference Operator
- Updated and developed a user-friendly conference environment and real-time video streaming
- Optimized the overall conference experience by implementing customizable features for flexible setup
- Enhanced the efficiency and usability of conference technology, enabling a seamless workflow and improved participant interaction experience
Raghu Ram V.
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Tobias B.
Last position:
Lead XR Project at BMW Group
- Showcasing the world's first fully immersive AR glasses experience in a moving car at CES 2024.
- Speaker about augmented reality at international conferences (e.g. the AR Ride Concept @ Unite 2024).
- Lead a 12-person interdisciplinary software team developing Android head-unit integrations, navigation & ADAS UI, and embedded software.
- Define technical direction, drive cross-domain architecture and integration, and mentor engineers across Android, UI/UX and embedded stacks.
- Oversee a small fleet of test vehicles for validation, tests, and data collection.
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Marwa H.
Last position:
Computer Vision Project
- Developed a convolutional neural network (ConvNet)-based model that achieved 95% accuracy in traffic sign recognition and classification.
- Programming language: Python 3.7.
- Libraries: Numpy, matplotlib, scikit-learn, scikit-image.
- Deep learning framework: Tensorflow.
Suzan K.
Last position:
Digital Marketing & Analytics; Identity and Access Management at Rohde & Schwarz GmbH & Co. KG
- Webinar moderation.
- Created quarterly KPI reports using Adobe Analytics, Sprinklr, and M4C.
- Researched and proposed AI tools for marketing process optimization; pitch decks, integration planning.
- Defined customer personas, analyzed competitor social media activity, and improved CRM data quality.
- Executed IDM tasks related to user provisioning, access reviews, and role management.
- Performed integration testing with MidPoint, Active Directory (AD), and SAP systems.
- Supported identity lifecycle management across onboarding, role changes, and deprovisioning.
Adithya B.
Last position:
Edge AI Software Engineer at Neura Robotics GmbH
- Deployed and optimized Vision-Language-Action (VLA) and diffusion policy models on NVIDIA Jetson Orin and Jetson Thor, meeting real-time inference latency targets for humanoid robot control loops.
- Built TensorRT engine pipelines (PyTorch → ONNX → TensorRT) with INT8/FP8 post-training quantization, calibration dataset design, and quantization-aware validation, reducing inference memory footprint by over 3× on Jetson without accuracy regression.
- Developed custom CUDA C++ plugins and CUDA Graphs for latency-deterministic, real-time policy execution – meeting hard runtime and memory constraints on embedded GPU targets.
- Developed an inference engine for VLA models on top of llama.cpp bringing different VLA policies under single runtime, packaging each as a single self-contained GGUF that needs no Python or PyTorch.
- Profiled and tuned GPU execution using NVIDIA Nsight Systems and Nsight Compute, identifying CUDA kernel bottlenecks, memory bandwidth saturation, and SM occupancy issues across Jetson Orin and Thor compute profiles for cross-layer performance optimization.
Discover over 15,000 top freelancers
Statistics of experts using Convolutional Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 11 years)
Position duration
2.5 years (Germany: 1.8 years)
Positions per freelancer
9 (Germany: 7)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Information Technology, Business Intelligence, Finance
Bachelor's degree or higher
100%
Master's degree or higher
100% (Germany: 93%)
Doctorate
30% (Germany: 15%)
Certifications per freelancer
1 (Germany: 2)
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using Convolutional Neural Network
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Convolutional Neural Network 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 (91%)
- Automotive (64%)
- Education (64%)
- Manufacturing (55%)
- Banking and Finance (36%)
- Healthcare (36%)
- Insurance (27%)
- Advertising (18%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Core principles
A Convolutional Neural Network, commonly called a CNN, is a deep learning model designed to interpret structured grid data such as images and video frames. Convolutional layers detect local patterns, while pooling and later layers combine them into increasingly meaningful features. The model learns these representations from labelled or self-supervised training data.
What it builds
CNNs support computer vision systems that need to recognise, locate or segment visual content. Typical deliverables include:
- Image classification for products, documents or medical imagery
- Object detection for vehicles, components or safety events
- Semantic and instance segmentation for precise image analysis
- Visual anomaly detection and automated quality inspection
- Image and video preprocessing pipelines for downstream models
Ecosystem and tooling
Strong specialists work across Python and deep learning frameworks such as PyTorch, TensorFlow and Keras. They select architectures such as ResNet, EfficientNet, U-Net or YOLO according to the data and latency requirements. Practical work also includes OpenCV, CUDA, GPU training, experiment tracking, data versioning and model serving with APIs or edge runtimes.
When expertise matters
Companies bring in freelance specialists when a proof of concept must become a reliable product, when labelled image data is limited, or when an existing model performs poorly outside the lab. In Munich, CNN projects can support manufacturing inspection, mobility, robotics, healthcare imaging and retail operations. Remote collaboration works well when datasets, environments and acceptance criteria are clearly documented; on-site sessions can help with cameras, sensors and factory equipment.
Delivery considerations
A credible project starts with a review of image quality, class balance, annotation rules and the cost of false predictions. Specialists define suitable validation splits, augmentation methods and evaluation measures before tuning the network. They also consider inference speed, memory use, privacy, monitoring and retraining rather than treating model accuracy as the only deliverable.
Signs of strong specialists
Look for professionals who can explain model behaviour and connect experiments to business outcomes. Strong candidates usually demonstrate:
- Reproducible training and evaluation workflows
- Careful dataset inspection and annotation strategy
- Transfer learning and fine-tuning across changing domains
- Deployment on cloud GPUs, servers or edge hardware
- Clear documentation for maintenance and handover
A good specialist can also identify when a CNN is not the right choice, compare it with transformer-based vision models, and make trade-offs understandable to product and operations teams.
Frequently asked questions
Before you brief your next project: the most common questions about Convolutional Neural Network.
A Convolutional Neural Network is used to analyse images and video by learning visual patterns such as edges, textures, shapes and object parts. Companies use CNNs for classification, detection, segmentation, optical character recognition, facial analysis and industrial inspection.
A CNN focuses on local spatial patterns through convolutional filters, which can make it efficient for many image and edge-processing tasks. Vision transformers model relationships across broader image regions and may perform strongly with large datasets, but the better choice depends on data volume, latency, hardware and deployment constraints.
A strong Convolutional Neural Network specialist often also works with Python, PyTorch or TensorFlow, image labelling, OpenCV, GPU environments and data pipelines. Experience with MLOps, experiment tracking, APIs, cloud infrastructure and edge deployment is valuable when the model must run in production.
The required background for a CNN project depends on its risk and complexity, not just on the model type. A straightforward classification proof of concept may need focused modelling experience, while regulated imaging, safety-critical detection or real-time robotics requires proven work with validation, deployment and monitoring.
Yes, Convolutional Neural Networks can usually be developed remotely when data access, compute environments and evaluation criteria are available online. On-site collaboration in Munich becomes more useful when the work involves cameras, production lines, robotics or sensor calibration. German or English communication can be agreed according to the project team.
Before hiring for a CNN project, define the visual task, available images, annotation status, target environment and business cost of errors. Access to representative data and a clear acceptance test helps a specialist estimate feasibility and avoid optimizing for an unrealistic benchmark.
A reliable Convolutional Neural Network solution is evaluated on representative, unseen data rather than training results alone. Review class-level errors, robustness to lighting and camera changes, inference latency, resource use and performance after deployment. Reproducible experiments and clear documentation are also essential.
A CNN pretrained on a broad image dataset is often a practical starting point when labelled project data is limited. Fine-tuning can adapt its learned features to specialised images, but the dataset still needs careful review for domain shift, class imbalance, leakage and licensing requirements.
The average hourly rate of freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects is 94 €, which corresponds to a daily rate of about 754 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 30% hold a doctorate.
On average, freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are German (100%), English (100%), and French (36%).
The most common industries among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are Information Technology (91%), Automotive (64%), and Education (64%).
The most common business areas among freelancers in Munich, Germany who have used Convolutional Neural Network in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (100%).
Main locations of FRATCH Experts, who have recently used Convolutional Neural Network
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.
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
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FRATCH CEO
