Recurrent Neural Network Experts in Munich
matched in minutes with vetted specialists and the power of AI.Hire experts who design sequence models, tune RNN, LSTM, and GRU architectures, and build text, speech, and time-series solutions with clean training pipelines. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used Recurrent Neural Network
René Welland
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 Vadali
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.
Martin Ratajczak
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)
Oussama El Allam
Last position:
Head of R&D at eXagotec GmbH
- Spearheading multidisciplinary engineering teams in the development of next-generation medical devices
- Orchestrating research initiatives and technology roadmaps to deliver innovative medical solutions
- Overseeing R&D budget and managing project portfolios from concept through to commercialisation
- Establishing strategic collaborations with clinical partners for technology validation
Borui Li
Last position:
Spectral Analysis of Neural Network Kernels at Borui Li Projects
- Explored the impact of neural network structure on network-inspired kernels, such as Neural Tangent Kernel (NTK).
- Demonstrated through theoretical analysis and empirical studies that the RKHS of NNGP is a subspace of NTK.
- Explored the connections between these kernels and the Matérn family.
Daniel Carton
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)
Anton Klonov
Last position:
Head of Technical Overall Integration NSC / Hadoop Cloud Development at IABG
Head of technical overall integration NSC (National Secure Cloud project with about 60 employees).
Technical integration of all subprojects into one product, definition of interfaces, basic components of a cloud including hardware, technical architecture of the IABG base.
Development of a Cloud Management Platform (CMP) that can create a private/mixed cloud of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management cycle.
As a foundation, it uses Kubernetes, OpenStack, and Hadoop.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are automatically configured.
The private cloud can run any customer workloads, including a full Hadoop stack with HDFS, Spark, MapReduce, Mesos, HBase and around 20 other ML/DL technologies.
Hadoop worker clusters can also be automatically installed on bare metal or commodity hardware without Kubernetes.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, database.
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).
Adithya Balaji
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 Recurrent Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
2.5 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Healthcare, Automotive
Certification focus areas
Business Intelligence, Information Technology, Product Development
Bachelor's degree or higher
86%
Master's degree or higher
86%
Doctorate
29%
Certifications per freelancer
1
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Munich 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 Munich using Recurrent 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What RNNs do
Recurrent Neural Networks handle ordered data where each step depends on the one before it. They are used for text, speech, sensor streams, forecasts, and other sequence tasks. In Munich, companies often bring in RNN specialists for product teams that need models close to real operational data.
Common use cases
- Language modeling and text generation
- Speech recognition and audio features
- Time-series prediction and anomaly detection
- Sequence labeling for logs, events, or signals
RNN work often sits inside broader machine learning systems, not as a standalone model. It can also serve as a baseline before moving to attention-based models.
Tooling and stack
Strong specialists usually work with TensorFlow, PyTorch, Keras, NumPy, and standard Python ML tooling. They understand embedding layers, hidden states, backpropagation through time, and how to monitor training stability. For Munich teams, clear handover into existing data and ML stacks matters as much as model choice.
When freelance help fits
Companies often need freelance expertise when a model must be prototyped quickly, debugged, or reviewed before release. That includes inherited code, poor training results, missing documentation, or a need to compare an RNN against newer sequence methods. Freelancers also help when a local team needs short-term support without adding permanent headcount.
What strong experts bring
A good RNN specialist explains trade-offs in plain terms and knows when a simple recurrent setup is enough. Look for practical work on data preparation, sequence length handling, evaluation, and deployment constraints. They should also know where LSTM and GRU improve on basic recurrent cells and where they do not.
What to expect in Munich
Munich companies in industrial tech, mobility, analytics, and applied research often use recurrent models with internal data and strict integration rules. Some collaboration happens on-site when data access is sensitive, while model development and review can stay remote. Clear English is common, and German helps when teams work closely with local stakeholders.
Frequently asked questions
Questions about Recurrent Neural Network? Start with the answers below.
A Recurrent Neural Network is used for data that arrives in order, such as text, audio, signals, and time-series. It helps a system use earlier steps to interpret what comes next. That makes it useful for forecasting, sequence labeling, and language-related tasks.
A Recurrent Neural Network is the broader family, while LSTM and GRU are more specialized variants designed to keep useful context longer. In many real projects, those variants train more reliably on longer sequences. A good expert should explain which choice fits your data, not just default to the newest model.
Choose RNN-based approaches when sequence order matters and the use case is well understood, especially in compact or legacy systems. They can also be easier to integrate when the data pipeline is already built around recurrent layers. For many teams, the decision comes down to latency, complexity, and maintenance.
A strong Recurrent Neural Network specialist usually brings Python, TensorFlow or PyTorch, data preprocessing, and model evaluation skills. They should also understand embeddings, sequence padding, training stability, and deployment basics. If your data is messy, experience with feature engineering and labeling is valuable too.
A Recurrent Neural Network project can be simple if you only need a baseline or a small proof of concept. It becomes more demanding when sequence lengths vary, labels are noisy, or the model must run in production. For that reason, you want someone who has shipped more than notebook experiments.
Yes, most RNN work can be done remotely if the data access, review process, and handover are set up well. On-site time in Munich helps when teams need close collaboration, access to protected data, or faster alignment with product and research stakeholders. Many projects use a mixed setup.
A strong Recurrent Neural Network expert can explain data choices, training issues, and evaluation results in simple language. Look for clear version control, reproducible experiments, and honest trade-offs between basic recurrent cells and LSTM or GRU variants. Good specialists also document what failed, not only what worked.
A Recurrent Neural Network often struggles when sequences are very long, labels are sparse, or the data has weak signal. Poor preprocessing, inconsistent timestamps, and leakage between train and test sets are common issues. A careful specialist will test these risks early and adjust the pipeline before tuning the model.
The average hourly rate of freelancers in Munich, Germany who have used Recurrent Neural Network in their recent projects is 93 €, which corresponds to a daily rate of about 746 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Recurrent Neural Network in their recent projects, 86% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 29% hold a doctorate.
On average, freelancers in Munich, Germany who have used Recurrent Neural Network in their recent projects have 17 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 Recurrent Neural Network in their recent projects are German (100%), English (100%), and French (38%).
The most common industries among freelancers in Munich, Germany who have used Recurrent Neural Network in their recent projects are Information Technology (88%), Healthcare (63%), and Automotive (50%).
The most common business areas among freelancers in Munich, Germany who have used Recurrent 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 Recurrent 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.
Countries:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
