Reinforcement Learning Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Reinforcement Learning
Philipp Grunert
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Thomas Martin
Last position:
Lead AI-/Agentic-Engineering at AI-/Agentic-Engineering (Own research)
AI-supported development and PM acceleration with agentic workflows; deep reinforcement learning; fully automated 24/7 setup.
Yimeng Wang
Last position:
R&D Software Engineer at Advantest
- Development and maintenance of hardware drivers in C++
- Conducting unit and integration tests to ensure code quality
- Debugging and fixing issues with the hardware team and FPGA team
- Defining and developing software concepts and coordinating with the software architect
- Expanding test automation to improve efficiency
- Research and development of algorithms to improve existing codebases (runtime, memory usage, accuracy)
Stefan Zeidler
Last position:
Agile Project Manager at Telefónica o2 Germany GmbH & Co. OHG
- Implementation of MVPs in fixed-line communication with a team of 3 technical product owners
- Building the product roadmap
- Defining epics with business units, breaking down into features and user stories
- Managing offshore development teams
- Providing transparency and reporting to the overall program
- Agile development using SAFe approach
David Thompson-Ajayi
Last position:
AI Trainer (NLP & LLM Evaluation) at Freelance
- Designed and evaluated high-quality prompts and completions for Large Language Models (LLMs), focusing on improving response accuracy, instruction-following behavior, and factual consistency.
- Annotated and rated LLM-generated outputs for grammar, coherence, relevance, and truthfulness.
- Developed RLHF-style preference data by ranking model completions to inform reinforcement learning fine-tuning cycles.
- Participated in prompt engineering experiments to assess the effect of instruction format, verbosity, and phrasing on model behavior.
- Conducted error analysis and quality assurance on large-scale NLP datasets, identifying edge cases and linguistic ambiguity affecting LLM performance.
Vasco Almeida
Last position:
AI Research Intern – Generative AI at BMW AG
- Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
- Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
- Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.
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)
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).
Haoyuan Chen
Last position:
Software Engineer – Backend Development at AICI GmbH
- Independently led backend development as the sole contributor and applied computer vision techniques to transform raw SLAM (Simultaneous Localization and Mapping) data into user-friendly CAD models, advancing the product from prototype to release-ready for architectural applications
- Developed and implemented mathematical algorithms to accurately detect room contours and improve the precision of wall-length estimations from spatial maps
- Contributed to reducing human intervention by optimizing backend processes for real-time, automated CAD generation
- Collaborated with cross-functional teams in robotics, data science, and software engineering to enhance system efficiency and scalability
- Stack: Python, C++, OpenCV, NumPy
Janusz Mazurek
Last position:
IoT Edge Computing / Self-Driving-Cars at Automotive consulting company
- Platform: Python ecosystem, RHEL 8, K10, AWS IoT Core, AWS Lambda, MLOps
- Software: Java JEE/cloud, IntelliJ IDEA, AWS IoT Core, AWS Edge and Lambda, AWS SageMaker SDK, Docker Compose, Kubernetes, OpenShift 4, Tekton, Flux, Helm charts, JSON/XML technology, Nginx, Apache Spark, OpenAI (GPT Plus, DALL-E 3, Whisper), GAN, GitHub Copilot, AI/machine and deep learning, Jupyter notebooks, TensorFlow 2, Colab, Keras API, Prometheus, Grafana, Conda, Python 3.9, PySci stack (NumPy, pandas, Scikit-learn, matplotlib)
- Responsible for webinar:
- IoT edge computing: architecture, components, resources, management
- IoT edge computing with MicroK8s, designing and creating flows/diagrams for AWS, three-step model for IoT ecosystem
- IoT processes, connectivity, data transfer and deployment, security
- Optimization of edge computing for IoT networks and services (AWS SQS queue, SNS notifications, events, analytics, buttons, device management/defender, Things Graph)
- Machine/deep learning frameworks (models, training, pipeline optimization, deployment in the cloud/at the edge (OpenShift), monitoring workloads with Prometheus and Grafana)
- Performance optimization for low latency/resilience using adaptive ML/DL/RL models for customer IoT data
- Analysis of large sensor data sets with Apache Spark, Kafka clusters
- Kasten K10 data management platform on Kubernetes multi-cluster with Helm chart, deployment, backup/disaster recovery (RTO/RPO), data lifecycle and security management
- Implementation of multilayer artificial neural network (ANN) with TensorFlow 2 and Colab for regression and classification; data analysis and provisioning for applications; development of models for testing and training, deployment of models
- Automation of business streamline processes with AI (Azure OpenAI, Discord bots/Zapier apps AI assistants (IntelliJ, GitHub Copilot))
Discover over 15,000 top freelancers
Statistics of experts using Reinforcement Learning
Aggregated from the professional profiles of matched freelancers.
Experience
18 years
Position duration
1.8 years
Positions per freelancer
14
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Automotive, Education
Certification focus areas
Project Management, Research and Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
17%
Certifications per freelancer
1
Most common languages
English, German, 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 Reinforcement 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it is
Reinforcement learning, often called RL, is used to train systems that learn by trial and feedback. It fits problems where a model must choose actions, adapt to changing conditions, and improve over time, such as robotics, bidding, routing, or recommendation tuning.
Typical work
- Define states, actions, and reward logic
- Build training loops and simulation environments
- Compare policies with offline and live evaluation
- Stabilize training with proper exploration and constraints
Tools and methods
Strong specialists work with Python, PyTorch, TensorFlow, Gym-style environments, and experiment tracking tools. They understand policy gradients, Q-learning, value functions, and when deep reinforcement learning is actually the right choice instead of a simpler supervised approach.
When companies need help
Teams usually bring in freelance expertise when an RL problem is still unclear, when a prototype needs to become reliable, or when simulation and reward design need careful review. In Munich, this is common in mobility, industrial systems, and research-heavy product teams that need practical delivery, not theory alone.
What good experts deliver
Good professionals turn a vague decision problem into a testable setup. They document assumptions, design clear benchmarks, and explain trade-offs between sample efficiency, stability, and business impact. They also know when to stop training and fall back to a safer baseline.
How to judge fit
Look for people who can explain reward shaping, exploration, and evaluation without hand-waving. Ask for examples that show how they handled sparse feedback, delayed rewards, or constrained action spaces. The best specialists make the whole loop understandable for the team, from data to deployment.
Frequently asked questions
Key details about Reinforcement Learning, drawn from the questions we get asked most.
Reinforcement Learning is used for problems where a system must choose actions and learn from feedback. Common cases include robotics, dynamic pricing, traffic control, scheduling, and recommendation tuning. It matters most when the next decision depends on past outcomes, not just on a fixed input-output map.
RL is different because the training signal comes from rewards, not labeled examples. Supervised learning is better when the target is known, while RL fits sequential decisions with delayed results. In many projects, teams start with a simpler baseline before moving to RL.
A company should hire a reinforcement learning specialist when the problem involves decisions over time, sparse feedback, or complex trade-offs. This is common when reward design, simulation, or policy evaluation is hard enough that a generalist can only go so far. A specialist helps avoid costly trial-and-error.
A strong RL expert usually knows Python well and can work with PyTorch or TensorFlow. They should also understand statistics, experiment design, simulation, and product constraints. For real projects, knowledge of data pipelines and deployment is often just as important as the algorithm.
You do not need a fully defined project before engaging a Reinforcement Learning expert. You do need a clear decision problem, a rough idea of the reward, and access to the data or environment that will drive learning. Good specialists can help shape the problem before the first model is trained.
Yes, Reinforcement Learning work is often remote because much of it happens in notebooks, simulators, and code review. For Munich teams, remote collaboration works well when the domain is documented and stakeholders can meet on a clear schedule. On-site time can still help when hardware, lab systems, or sensitive data are involved.
Look for clear reasoning, not just model names. A strong RL freelancer can explain reward shaping, exploration, baseline comparisons, and why one policy is safer than another. Ask for past work that shows measurable decision quality, stable training, and thoughtful evaluation.
The main alternatives to reinforcement learning are supervised learning, rule-based systems, and optimization methods like linear programming or search. These options are often easier to validate when the decision space is small or the feedback is immediate. RL becomes attractive when the environment changes and decisions affect future outcomes.
The average hourly rate of freelancers in Munich, Germany who have used Reinforcement Learning in their recent projects is 89 €, which corresponds to a daily rate of about 713 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Reinforcement Learning in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Munich, Germany who have used Reinforcement Learning in their recent projects have 18 years of professional experience, with a single engagement typically lasting around 1.8 years.
The most common languages among freelancers in Munich, Germany who have used Reinforcement Learning in their recent projects are English (100%), German (92%), and French (33%).
The most common industries among freelancers in Munich, Germany who have used Reinforcement Learning in their recent projects are Information Technology (92%), Automotive (67%), and Education (58%).
The most common business areas among freelancers in Munich, Germany who have used Reinforcement Learning in their recent projects are Information Technology (100%), Research and Development (92%), and Product Development (83%).
Main locations of FRATCH Experts, who have recently used Reinforcement 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.
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
