High-Performance Computing Experts in Germany
for demanding simulations, analytics and AI workloads with fast, precise matchingHire experts who design parallel workloads, tune distributed systems and manage scientific computing environments across CPU, GPU and cloud infrastructure. FRATCH connects you quickly with vetted, available freelancers whose skills match your technical requirements.
Meet FRATCH Experts in Germany, who have recently used High-Performance Computing
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Peter S.
Last position:
Senior ML Engineer & AI Researcher at Anonymous Client
Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing
- Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
- Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
- Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.
Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Afaq A.
Last position:
Master’s Thesis Researcher – Multiview Perception Evaluation at Volkswagen AG
- Developed an evaluation framework for AI-generated multiview driving videos intended for perception and embodied-AI/VLA-related training workflows.
- Designed automated checks for temporal coherence, cross-camera consistency, semantic correctness, and multiview geometric quality, exposing failure modes relevant to autonomous systems.
- Combined classical computer vision, learned visual representations, and vision-language models to convert complex video artifacts into measurable engineering signals.
- Built repeatable benchmarking and failure-analysis workflows to support model comparison, data-quality decisions, and system-improvement discussions.
Ananthraj N.
Last position:
Founder at sprhava
Leading the end-to-end development of Edge AI-powered smart glasses for visually impaired individuals, aligning product vision with user needs and managing a cross-functional team of data scientists, Android developers, AWS engineers, and hardware specialists.
- Defined product roadmap for Edge AI smart glasses and MVP features through user research, stakeholder interviews, and competitive analysis, ensuring accessibility and real-world usability.
- Developed and validated a PoC for AI-driven cancer cell identification in PET/CT scans, collaborating with medical experts to optimize diagnostic accuracy and clinical relevance.
- Built and scaled a multidisciplinary team of 75+ engineers, interns, and designers across Germany and India, driving cross-border collaboration and iterative prototyping.
- Established strategic partnerships with NGOs, healthcare providers, and advocacy groups to embed inclusivity and patient feedback into product design.
- Drove hands-on hardware-software integration using Raspberry Pi and Jetson Nano of AI models. Initiated and nurtured relationships with suppliers, manufacturers, and ecosystem players to build scalable go-to-market plans.
- Owned critical product decisions, from prototype development to funding strategy, applying a data-informed and impact-driven mindset.
- Fostered a learning-focused culture by facilitating brainstorming sessions and continuous feedback loops between engineering and product.
Achievements:
- Public speaker: Auto.Ai 2025 (Berlin), Wearable technologies 2025 (Munich and Bangalore), MEDICA 2024 (Dusseldorf)
- WMF, Bologna, Italy (June 2024): Only AI startup to be selected from Germany as EBV hero to represent sprhava on global platform
- Medica, Germany (2024): Delivered a speech on AI smart glasses in world's largest Healthcare event.
- Wearable Technologies, Bengaluru, India (Dec 2024): I was a speaker presenting sprhava and its product.
- Venturise Global Challenge (GIM 2025, Bengaluru Palace, Karnataka): sprhava was selected as one of the 16 top startups (ESDM) to present on this global platform
- Wearable Technologies Conference 2025 EUROPE, Munich, Germany (May 2025): Delivered a talk on Edge AI at the Europe's biggest wearable tech event.
- InsurNext Köln, Germany (2025): sprhava was honoured with a booth from Cologne administration.
Heena P.
Last position:
Retirement Spend & Tax Optimizer Agentic AI App (Vibe Coding) at Personal Project
Self-directed exploration of agentic AI development methods, taken from idea to a working, publicly usable application
- Built an interactive planning tool for modelling retirement withdrawals and tax strategy using an agentic AI (vibe coding) development approach – demonstrating self-directed investigation of new AI-assisted development methods
- Delivered live, tax-aware spending projections and adjustable user inputs; shipped as a free, install-free browser application built in Python, with attention to usability for non-technical users
Serge K.
Last position:
MLOps (machine learning operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
Chenchen C.
Last position:
Patent Engineer (European patent attorney candidate) at Vossius & Partner
- Patent application: European patent drafting and prosecution
- LLM practicing: Developed LLM-based tools for automated patent data retrieval, applying Python scripting to accelerate technical reviews.
Dirk Markus M.
Last position:
Scientific Software Consulting Engineer
Technical audit for scientific software.
Oliver O.
Last position:
Embedded Software Architect at Automotive supplier
Stellar SR6 G7 line, 32-bit Arm® Cortex®-R52+ MCU.
- MISRA-C, C99, Greenhills ARM compiler
- Dassault AUTOSAR Builder
- EB Tresos
- Sparx Enterprise Architect 16.1
- VS Code
- Python xml, lxml, NumPy and Pandas
ISO 26262, ISO 21434, hypervisor, key management, HSM. Tooling & automation. LieberLieber LemonTree + Sparx EA. Jira, Confluence, SharePoint. Git/Github. DevOps through Jenkins & Conan.
Shiqing F.
Last position:
Technical Director at EmotionPool GmbH
- Spearheaded the EU market entry strategy for L3/L4 autonomous logistics vehicles, driving the technological localization and deployment of the parent company’s smart robotics portfolio.
- Orchestrated technical alignment between top-tier autonomous driving suppliers across China and Europe, translating complex client requirements into precise engineering specifications compliant with EU standards.
- Cultivated strategic joint R&D initiatives with leading European universities, research institutes, and enterprises, accelerating the transition of cutting-edge robotic concepts into commercial products.
- Directed the end-to-end architecture of intelligent warehousing solutions, guiding cross-functional teams in optimizing hardware integration for autonomous vehicles & robots, and overall system performance.
- Led the R&D of high-fidelity simulation and AI algorithms using NVIDIA Isaac Sim & Lab, establishing robust "Sim-to-Real" pipelines to train and validate dynamic path planning optimization, intelligent obstacle avoidance, and complex navigation stacks prior to physical deployment.
- Maintained hands-on oversight of the core system architecture, focusing on bottom-level performance tuning, AI model inference acceleration with TensorRT/ONNX Runtime, and sensor integration.
Alexander S.
Last position:
AI Consultant for AI Voice Bot System at Rudolf Hörmann GmbH & Co.KG
- Consultant for system architecture, AI agents & integration, coach for data & process logic, Graph-RAG approaches, security and data protection.
- On-premise AI solutions with high compliance and performance requirements.
- Architecture decisions, operational setup, strategic prioritization & deployment.
- Technologies: LiveKit JS SDK, LiveKit Agents, Web Audio API, JS, AudioWorklet, Loki, vLLM, Zscaler, Docker, Neo4j, MySQL, Python.
- Models: GPT-OSS 20B, Whisper large v3 turbo, Qwen3-TTS.
André B.
Last position:
External Attack Surface Assessment & Cybersecurity Readiness Checks at Graydaxe Cybersecurity GmbH
- Conducting cybersecurity readiness checks based on an in-house assessment methodology
- Analyzing the external attack surface using the Graydaxe EASM platform
- Assessing maturity levels and deriving prioritized recommendations for action
Muhammed A.
Last position:
AI System & Product Lead at awRAG.io & Laiers.ai
Conception, planning, and production deployment of two AI platforms for industrial research and engineering workflows, from use-case identification and requirements analysis through architecture decisions and build-vs-buy trade-offs to go-live.
awRAG.io: Identification of the use case (fragmented knowledge base across distributed AI tools), definition of data requirements, architecture decision for a multi-tenant RAG-as-a-service platform with GDPR-compliant EU infrastructure and production-grade retrieval pipeline
LAIERS.ai: Use-case definition (context loss in linear AI workflows), strategic product decisions on UX, cost structure, and multi-LLM orchestration, rollout of a spatial AI conversation platform with proprietary context management system LAICS
LLMOps ownership: Quality assurance, pipeline optimization, security architecture (OAuth 2.0, SOC 2), and performance monitoring of both platforms in live production
Core topics: LLM, RAG, vector databases, LLMOps, AI architecture strategy, cloud infrastructure, data sovereignty
Gernot L.
Last position:
Founder and Managing Director at Softwerk/Ruhr GmbH
- Architecting and developing SaaS platform for graphical definition and execution of pandas data processing pipelines
- Developed POlyglott, an open-source Python CLI tool for translation workflow management featuring PO file parsing, quality linting with glossary enforcement, and DeepL API integration for machine translation
- Developed web application for material compliance management (EU REACH) using Django and modern web technologies
- Built automated infrastructure platform using Proxmox, Terraform, and Ansible — VM provisioning, configuration management, internal DNS, and fleet-wide security hardening across multiple subnets
Arne H.
Last position:
Embedded Fullstack Developer at IoT / Infrastructure Automation Sector
- Analysis of legacy codebase and identification of architectural issues, implementing improvements in coordination with the Product Owner.
- Development of clean, efficient, and fully documented code following established software engineering practices and standards.
- Analysis of Erlang components in backend and device layers to provide recommendations for ensuring stable system operation.
- Setup and optimization of CI/CD pipelines on client infrastructure, including testing, debugging, and certificate management quality assurance.
- Participation in planning, design, and implementation of epics and stories according to Product Owner specifications.
- Technical consultation for Product Owner regarding Erlang codebase management and best practices.
- Collaboration with Product Owner, Scrum Master, and development team to ensure timely delivery of features.
- Elixir & Phoenix + PostgreSQL
- Erlang
- IoT
- CI/CD
- Git
- Agile/Scrum
- Docker
- Kubernetes
- Frontend (VueJS)
- Embedded Devices
Discover over 15,000 top freelancers
Statistics of experts using High-Performance Computing
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
1.8 years
Positions per freelancer
9
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Education, Information Technology, Automotive
Certification focus areas
Information Technology, Product Development, Quality Assurance
Bachelor's degree or higher
97%
Master's degree or higher
90%
Doctorate
36%
Certifications per freelancer
2
Most common languages
English, German, French
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 Germany 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 Germany using High-Performance Computing
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.
High-Performance Computing experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Education (64%)
- Information Technology (59%)
- Automotive (43%)
- Manufacturing (36%)
- Energy (34%)
- Biotechnology (27%)
- Healthcare (23%)
- Banking and Finance (20%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What HPC delivers
High-Performance Computing, commonly called HPC, combines powerful processors, memory, storage and high-speed networks to solve computationally demanding problems. Companies use it for scientific simulations, engineering analysis, weather modelling, life sciences, financial risk and large-scale machine learning. Workloads are split across many cores or nodes so results arrive faster than with a single system.
Systems and tooling
HPC environments span on-premises clusters, public cloud resources and hybrid infrastructures. Professionals work with Linux, batch schedulers such as Slurm, MPI, OpenMP, CUDA, ROCm, container runtimes and parallel filesystems. They select hardware, configure interconnects, automate provisioning and connect monitoring with resource management. Germany’s research, automotive, manufacturing and energy sectors often combine local clusters with cloud capacity for changing workloads.
Typical workloads
- Computational fluid dynamics and finite element analysis
- Molecular dynamics, genomics and medical research
- Seismic processing, climate models and weather forecasting
- GPU-accelerated machine learning and data analytics
- Monte Carlo simulation, optimisation and risk analysis
When companies need specialists
Freelance expertise is useful when a cluster is underused, jobs run unpredictably or a simulation cannot scale efficiently. Companies also bring in specialists during platform migrations, GPU adoption, scheduler changes and software modernisation. The right professional can profile bottlenecks, improve parallel efficiency and leave behind repeatable deployment and operating practices.
Skills that matter
Strong professionals understand both application code and the infrastructure that executes it. They can reason about concurrency, memory locality, communication overhead, numerical accuracy and reproducibility. Relevant experience may include C, C++, Fortran or Python alongside MPI, OpenMP, CUDA, performance profilers, CI/CD and infrastructure automation. Clear documentation matters because HPC teams often span research, IT and operations.
Choosing the right professional
Look for evidence that the specialist improved a workload on the same type of processor, accelerator, scheduler or interconnect your project uses. Ask how they establish a baseline, isolate bottlenecks and validate results after optimisation. For remote work, secure cluster access, documented runbooks and scheduled coordination are usually enough; on-site collaboration can help with hardware integration, restricted environments or teams that require German-language communication.
Frequently asked questions
Questions about High-Performance Computing? Start with the answers below.
High-Performance Computing is used for workloads that require more processing power, memory or parallel capacity than a standard server can provide. Typical applications include engineering simulation, molecular research, climate modelling, financial analysis, image processing and large machine learning workloads.
HPC focuses on tightly coupled parallel workloads, specialised hardware and fast communication between compute nodes. Cloud computing provides flexible access to infrastructure, and cloud HPC can combine that flexibility with clusters, GPUs, high-speed storage and schedulers suited to demanding jobs.
A strong High-Performance Computing specialist may also understand Linux administration, C++, Fortran or Python, MPI, OpenMP, CUDA, Slurm, containers and infrastructure automation. Profiling, numerical methods, data engineering and security are valuable when the work spans application code and platform operations.
The right level depends on the scope. A professional tuning one CUDA kernel needs different depth from someone designing a cluster, migrating workloads or setting scheduler policy. Review comparable workloads, measurable optimisation methods and experience with the hardware and software used in your environment.
High-Performance Computing work can often be done remotely when secure access, test data and monitoring are available. On-site time may be useful for hardware installation, restricted research environments or workshops, while German or English communication can be agreed according to the team and documentation.
A good HPC engagement should produce a documented baseline, profiling results, implemented improvements and a reproducible way to run and measure the workload. It may also include scheduler configuration, deployment automation, monitoring guidance and knowledge transfer for the internal team.
Judge High-Performance Computing work by more than shorter runtime. The result should preserve numerical correctness, scale predictably, use resources efficiently and remain maintainable. Ask for a clear measurement method that separates application gains from changes in hardware, input size or system configuration.
Choose a GPU-focused HPC professional when the workload contains substantial data-parallel operations that can benefit from accelerators. A CPU-focused specialist may be better for branch-heavy code, memory-bound jobs or systems where MPI and general cluster scheduling are the main concerns; many projects need both perspectives.
The average hourly rate of freelancers in Germany who have used High-Performance Computing in their recent projects is 89 €, which corresponds to a daily rate of about 709 € based on an 8-hour working day.
Of the freelancers in Germany who have used High-Performance Computing in their recent projects, 97% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 36% hold a doctorate.
On average, freelancers in Germany who have used High-Performance Computing 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 Germany who have used High-Performance Computing in their recent projects are English (100%), German (98%), and French (20%).
The most common industries among freelancers in Germany who have used High-Performance Computing in their recent projects are Education (64%), Information Technology (59%), and Automotive (43%).
The most common business areas among freelancers in Germany who have used High-Performance Computing in their recent projects are Research and Development (91%), Information Technology (84%), and Product Development (70%).
Main locations of FRATCH Experts, who have recently used High-Performance Computing
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 
Hamburg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
