CUDA Experts in Munich
in minutes with vetted specialists and precise AI matchingHire experts who optimize GPU kernels, tune memory transfers, and build parallel workloads in CUDA, CUDNN, and related NVIDIA tooling. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used CUDA
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
Thomas Hoefkens
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
- Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
- Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
- Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
- Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
- Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
- Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integration of RESTHeart to create a REST API for MongoDB.
- Build an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).
Thomas Langer
Last position:
Consultant for AI-driven process automation at Lumiz
AI-driven automation of purchasing on a printing company's website, including selecting delivery times, order options, ordering, payment, and uploading print data from the Lumiz Cloud.
Andreas Blum
Last position:
Project Lead, Digital Transformation at SV Linde Tacherting e.V.
Researched, developed, and implemented comprehensive digital strategy to modernize and accelerate processes of sports club with approximately 1300 members.
System Architecture & Implementation: Conceived and set up central cost- and energy-efficient ARM-based server infrastructure.
Selected, installed, and configured open-source solutions for knowledge management, ticket booking, and member management.
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.
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)
Paul Richter
Last position:
Graphical Neural Network Builder at Independent Researcher
- Designed a web-based interface (React + Node + AWS EC2) allowing users to visually create neural networks and download them as PyTorch models.
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 CUDA
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
1.8 years
Positions per freelancer
9
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Manufacturing, Automotive
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
43%
Certifications per freelancer
2
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 CUDA
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
GPU compute
CUDA is NVIDIA’s programming model for running work on the GPU instead of the CPU. Teams use it for simulation, image processing, inference, and other compute-heavy tasks where parallel execution matters. Strong specialists know when CUDA is the right fit and when a mixed CPU and GPU design is better.
Common work
- Write and tune CUDA kernels for parallel workloads
- Move data efficiently between host memory and GPU memory
- Profile bottlenecks with NVIDIA tools and fix memory stalls
- Integrate CUDA into larger C, C++, or Python systems
Ecosystem
CUDA rarely stands alone. It is often paired with cuBLAS, cuFFT, cuDNN, Nsight, and modern C++ toolchains, plus Python bindings for research or production pipelines. Good experts understand the full stack, from driver behavior to how the application feeds the GPU.
Where it fits
Companies bring in CUDA specialists when performance is limited by large matrices, dense transforms, video pipelines, or scientific workloads. In Munich, this often comes up in robotics, industrial software, imaging, mobility, and research teams that need on-site collaboration for hardware access or a remote specialist for focused tuning.
What strong experts do
Strong CUDA professionals think in occupancy, memory bandwidth, latency, and kernel launch costs. They reduce transfers, choose the right data layout, and keep code maintainable across GPU generations. They also document trade-offs so the rest of the team can support the code later.
Engagement signs
- The current code is correct but too slow on large data sets
- GPU code needs review after a migration or hardware change
- A prototype must become production-ready and testable
- The team needs help with profiling, debugging, or kernel redesign
Frequently asked questions
Curious about CUDA? Here are the answers that come up again and again.
CUDA is used to run compute-intensive work on NVIDIA GPUs. Companies use it for simulation, signal processing, rendering, computer vision, and machine learning inference. It is a strong fit when the same operation must be applied to many data elements in parallel.
Choose CUDA when the workload targets NVIDIA hardware and performance matters enough to justify GPU-specific work. Compared with OpenCL, it usually offers deeper access to NVIDIA tools and a more mature ecosystem for profiling and tuning. Compared with CPU-only code, it can deliver far better throughput for parallel tasks, but only if data movement is controlled well.
A strong CUDA specialist should also understand C++ well, because most production GPU code depends on it. Useful adjacent skills include profiling, memory optimization, Python integration, and working with cuBLAS or cuDNN when the project uses standard primitives. For production work, testing and debugging skills matter just as much as raw kernel speed.
A small proof of concept may only need one focused specialist, but production work usually needs someone who has shipped and tuned GPU code before. CUDA is unforgiving when memory access, synchronization, or data layout are wrong, so prior project work is valuable. If the system is already in production, look for someone who can read profiling traces and explain trade-offs clearly.
Ask for concrete examples of performance work, not just language knowledge. A good CUDA expert can explain why a kernel is slow, what was changed, and how the change was verified with profiling or tests. Clear documentation, sensible naming, and careful handling of edge cases are also good signs.
Many CUDA tasks can be done remotely, especially profiling, kernel design, and code review. On-site work helps when the specialist needs direct access to hardware, lab systems, or a team that is still shaping the architecture. In Munich, both models are common, depending on whether the project is research-heavy or already in a stable engineering setup.
CUDA is the programming model and software stack for NVIDIA GPU compute. The CUDA Toolkit usually refers to the compilers, libraries, headers, and tools used to build and debug GPU applications. When people say NVIDIA CUDA, they are usually referring to the same ecosystem around GPU acceleration.
A weak CUDA freelancer may write code that looks correct but moves too much data, launches kernels badly, or ignores synchronization issues. That often leads to fragile performance and hard-to-debug crashes. The safest choice is someone who balances speed, correctness, and maintainability, not just raw optimization claims.
The average hourly rate of freelancers in Munich, Germany who have used CUDA in their recent projects is 89 €, which corresponds to a daily rate of about 714 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used CUDA in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 43% hold a doctorate.
On average, freelancers in Munich, Germany who have used CUDA in their recent projects have 14 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 CUDA in their recent projects are English (100%), German (88%), and French (38%).
The most common industries among freelancers in Munich, Germany who have used CUDA in their recent projects are Information Technology (100%), Manufacturing (75%), and Automotive (50%).
The most common business areas among freelancers in Munich, Germany who have used CUDA in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (88%).
Main locations of FRATCH Experts, who have recently used CUDA
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
