Recurrent Neural Network Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Recurrent Neural Network
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).
Borui L.
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 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)
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 Recurrent Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
18 years
Position duration
2.6 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Education
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
33%
Certifications per freelancer
1
Most common languages
German, English, Spanish
Speak two or more languages
100%
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 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 19 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Recurrent 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 (100%)
- Automotive (57%)
- Education (57%)
- Healthcare (57%)
- Manufacturing (57%)
- Banking and Finance (43%)
- Telecommunication (43%)
- Energy (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What RNNs do
Recurrent Neural Networks, or RNNs, are built for ordered data. They learn from what came before and use that context for the next step in the sequence. That makes them useful for signals, language, events, and other data that changes over time.
Where they fit
- Time-series forecasting and anomaly detection
- Text classification and sequence labeling
- Speech and audio pipelines
- Sensor and event stream analysis
RNN specialists also work with LSTM and GRU models when plain recurrent layers are not stable enough. They choose the right sequence setup for the data instead of forcing a generic model fit.
Tooling and stack
Strong professionals usually work in TensorFlow or PyTorch and know how to prepare sequential data carefully. They handle tokenization, padding, masking, batching, and evaluation for order-sensitive tasks. They also understand how RNNs interact with embeddings, feature windows, and downstream rules.
When companies bring in help
Teams usually look for freelance expertise when a sequence model is already in place but results are weak, slow, or hard to reproduce. They also bring in specialists when they need a proof of concept for text, audio, or demand data, or when they must move from a simple baseline to LSTM or GRU.
In Munich, this comes up in industries that work with manufacturing data, mobility signals, finance, and speech or language products. Remote work is common, but on-site sessions help when the data pipeline or stakeholder review needs close coordination.
What good specialists deliver
A strong RNN professional writes clear experiments and explains why a sequence model helps. They watch for leakage, vanishing gradients, weak baselines, and broken training windows. They can also compare recurrent models with CNNs or transformers and justify the simpler option when it is the better fit.
Signs you need one
- Your sequence baseline is not reliable
- You need model comparison against LSTM, GRU, or transformer options
- Your data has order, timing, or dependency issues
- You need clean handover notes and reproducible training runs
The best specialists do not only train a model. They make the full sequence workflow easier to maintain, test, and extend.
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, speech, sensors, and time series. It can use prior inputs to inform the next prediction, which makes it useful for sequence classification, forecasting, and labeling tasks.
RNNs are the basic sequence model, while LSTM and GRU are variants designed to handle longer dependencies more reliably. In many projects, experts start with a simple recurrent baseline and move to LSTM or GRU when training becomes unstable or the sequence memory is too short.
A strong Recurrent Neural Network specialist helps when a sequence problem is not solved by a standard tabular model or a simple rules approach. Companies also bring in help when they need better data preparation, model comparison, debugging, or a cleaner handover for production work.
A good RNN specialist usually knows Python, TensorFlow or PyTorch, and the basics of feature engineering for sequential data. Knowledge of embeddings, tokenization, batching, evaluation, and deployment workflows is also important, because the model is only one part of the system.
A Recurrent Neural Network project needs someone who has handled sequence data before, not just general machine learning work. If the data is noisy, long, or poorly labeled, the project benefits from a specialist who has dealt with training instability, leakage, and model selection.
Most RNN work can be done remotely if the data access, feedback loop, and security setup are clear. On-site sessions in Munich can still help when teams need close work on sensitive data, legacy pipelines, or fast alignment with domain experts.
Look for clear reasoning, reproducible experiments, and honest comparison with simpler baselines and newer alternatives. A strong Recurrent Neural Network expert should explain why the sequence model is needed, how they prevent leakage, and how they would validate the result in real use.
RNNs are still a good choice when the sequence is moderate, the system needs a compact model, or the task benefits from a simpler setup. For long-context language problems, transformers are often stronger, but a skilled specialist can tell you which option fits your data, budget, and deployment constraints.
The average hourly rate of freelancers in Munich, Germany who have used Recurrent Neural Network in their recent projects is 92 €, which corresponds to a daily rate of about 735 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Recurrent Neural Network in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 33% hold a doctorate.
On average, freelancers in Munich, Germany who have used Recurrent Neural Network in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 2.6 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 Spanish (29%).
The most common industries among freelancers in Munich, Germany who have used Recurrent Neural Network in their recent projects are Information Technology (100%), Automotive (57%), and Education (57%).
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
