Machine Translation Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Machine Translation
Hendrik Wagner
Last position:
Software Test and Maintenance Support at Anton Paar ProveTec GmbH
- Add/create test specifications
- Conduct regression tests
- Conduct release tests
- Analyze Jira tickets
- Identify software defects and fix them with C#
Tobias Nawa
Last position:
Enterprise & Solutions Architect
- Building an independent enterprise IT setup — cloud strategy, network, AWS landing zone, security requirements, contract negotiations.
- Migration of all applications; avoiding high contractual penalties for the client.
- Onboarding and coordination o...
Gernot Lang
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
Pawan Saxena
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Helmut Barz
Last position:
Service Provider for Writers and Self-Publishers at Selfpublisher-Verband
- Offered proofreading services and creation of supplementary texts
- Provided book design and typesetting
- Coached authors in conducting readings and presentations
- Developed e-learning offerings
- Managed the blog “FragDenWortSupport”
Rania Soudana
Last position:
Senior Software Engineer at Vermeg
- Designed and developed backend modules using Java, Spring Boot, and Hibernate
- Automated testing processes with JUnit and Selenium
- Used Postman for API testing
- Implemented CI/CD pipelines using GitLab, Docker, and Kubernetes
- Worked with Agile/Scrum methodologies, participating in daily standups, sprint planning, and retrospectives
- Maintained and managed SQL and NoSQL databases (PostgreSQL, MySQL, SQL Server)
- Conducted code reviews and performance optimizations
- Collaborated with cross-functional teams to ensure quality and timely delivery of software releases
Maciej Modrzejewski
Last position:
AI & Machine Learning Consultant at Self-employed
- Led the technical implementation of several AI products for companies, including defining the software architecture, leading distributed development teams of ML and software engineers, and coordinating delivery with executives.
- Developed and delivered 5+ production-ready AI products in the areas of machine translation, speech AI, document AI, conversational AI, and AI quality evaluation.
- Built a multilingual machine translation platform with over 550 production-ready models for automated translation of documents and business content in more than 40 languages.
- Built production-ready Conversational AI platforms using self-hosted Large Language Models (Qwen) with RAG pipelines, prompt engineering, tool calling, and secure enterprise deployments for internal knowledge assistants and customer-facing chatbots.
- Developed AI orchestration frameworks for dynamic selection of foundation models and for optimizing the quality, latency, robustness, and cost of production AI systems.
- Developed automated evaluation and monitoring pipelines for continuous quality assessment of Conversational AI systems, speech AI, and Large Language Models.
Aqsa Younus
Last position:
Multilingual Translation Tool - NLP Project
- Integrated MarianMT (Marian Machine Translation) models to ensure high-quality neural machine translation (NMT).
- Managed model loading and tokenization via Hugging Face Transformers, optimizing for offline caching and reproducibility.
- Planned extensions: language auto-detection, batch translations, and streamlined GPU inference with PyTorch.
Susann Heymann
Last position:
Freelance Translator & Editor at Freelance Translator & Editor
- Translation, post-editing, and localization of websites, apps, marketing content, product descriptions, and book translations.
- Ensured accuracy, natural flow, and cultural adaptation for German-speaking audiences.
- Applied machine translation tools like DeepL and consistently used style guides and glossaries for quality and consistency.
- Collaborated with clients in the nonfiction and fiction sectors, adapting content to specific target audiences.
- Gained personal experience using digital asset platforms, providing user-informed insights for localized content.
- Edited translations for websites, marketing campaigns, and apps, with a focus on fintech and e-commerce sectors.
- Post-edited MT output for clarity, consistency, and readability.
- Ensured terminological consistency and adherence to client style guides.
Uwe Wolfrum
Last position:
Freelance Translator & Content Manager at Altenar
- Translating weekly special insider reports on international online gaming, sports betting, gaming laws and the whole industry.
Britta Weber
Last position:
Freelance Translator, Editor, Text/Content Creator at Translation Weber
- Translation
- Proofreading/editing
- Website localization
- Post-editing
- Text/content creation
- Terminology work
- SEO texts/optimizations
- Sectors: IT industry (new technologies, cybersecurity, artificial intelligence, e-learning), digital marketing (websites, social media content), finance sector (finance, accounting, fintech)
- Text types (selection): website and marketing content, social media texts like blog posts and newsletters, brochures, short product texts, product documentation, technical data sheets, user interfaces, terminology databases, glossaries, software strings, manuals, case studies, customer success stories, white papers, technical presentations, training materials, process descriptions, work instructions, management reports, SLAs, financial reports, accounting guidelines, etc.
Discover over 15,000 top freelancers
Statistics of experts using Machine Translation
Aggregated from the professional profiles of matched freelancers.
Experience
20 years
Position duration
4.8 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Automotive, Manufacturing
Certification focus areas
Information Technology, Business Intelligence, Human Resources
Bachelor's degree or higher
100%
Master's degree or higher
73%
Doctorate
9%
Certifications per freelancer
1
Most common languages
German, English, French
Speak two or more languages
91%
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 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 Machine Translation
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 covers
Machine Translation turns text from one language into another with software. It is used for product content, help centers, support tickets, chat, search results, and internal knowledge. Strong specialists know when raw output is enough and when human review is still needed.
Common stacks
- Neural machine translation, or NMT, for modern text pipelines
- Google Translate, DeepL, and Microsoft Translator integrations
- OpenNMT, Marian NMT, and custom model workflows
- Glossaries, terminology rules, and quality checks
Where it helps
Companies bring in freelance professionals when they need fast rollout, better language quality, or help connecting translation systems to apps and content tools. In Germany, this often matters for SaaS, e-commerce, manufacturing, and customer service teams working across English and German.
What strong experts do
A strong specialist can prepare training data, test output quality, manage terminology, and reduce awkward phrasing in domain-specific text. They also know how to work with post-editing, language quality assurance, and feedback loops so the system improves over time.
Signs you need help
- Output sounds fluent but misses key terms
- Product copy or support text needs consistent terminology
- You want MT in a website, app, or CMS workflow
- You need help comparing NMT, SMT, and vendor APIs
- Review teams spend too much time fixing machine output
Delivery and collaboration
Machine Translation projects can run fully remote, which suits review, testing, and integration work well. On-site time only helps when teams need close input from content owners, legal reviewers, or local language stakeholders. Good professionals keep the process practical, transparent, and tied to real use cases.
Frequently asked questions
Key details about Machine Translation, drawn from the questions we get asked most.
Machine Translation turns source text into another language so teams can publish, support customers, or search content faster. It is most useful for repeatable text such as help articles, product descriptions, chat responses, and internal documents. The best setups balance speed with human review where accuracy matters.
Machine Translation is the broad field, while NMT means neural machine translation, the approach most teams use today. Older systems also include phrase-based and statistical machine translation, often shortened to SMT. When hiring, it helps to ask which approach the specialist has actually tuned and shipped.
Machine Translation can mean using vendor tools such as DeepL, Google Translate, or Microsoft Translator, but it also includes custom workflows. Off-the-shelf services are good for speed and simplicity, while tailored setups are better for terminology, privacy, and specific content types. A strong specialist knows when each option fits.
A strong Machine Translation professional usually understands terminology management, post-editing, language QA, and content workflows. For technical projects, API integration, CMS setup, and data handling matter too. If the work is domain-heavy, subject matter knowledge is just as important as language skill.
Machine Translation projects differ a lot, but most need someone who can handle both language quality and system behavior. Simple API integration may only need focused practical experience, while custom models and evaluation workflows need deeper expertise. Ask for examples that match your content type, not just general language work.
Yes, most Machine Translation work can be done remotely because the main tasks are testing, review, integration, and terminology work. In Germany, remote collaboration is common when teams are spread across content, product, and support functions. On-site sessions can still help when stakeholders need to agree on tone or glossary rules.
Look for clear evaluation methods, consistent terminology, and examples from the same language pair and content type. A good Machine Translation specialist explains what was fixed, why the output failed, and how quality will be measured over time. Strong work should read natural, stay faithful to the source, and fit the business context.
Use Machine Translation without editing only for low-risk text where speed matters more than polish. Add post-editing when the text is customer-facing, legally sensitive, brand-critical, or full of domain terms. The right specialist will help define that line instead of treating every text the same.
The average hourly rate of freelancers in Germany who have used Machine Translation in their recent projects is 75 €, which corresponds to a daily rate of about 598 € based on an 8-hour working day.
Of the freelancers in Germany who have used Machine Translation in their recent projects, 100% hold at least a Bachelor's degree, 73% hold at least a Master's degree, and 9% hold a doctorate.
On average, freelancers in Germany who have used Machine Translation in their recent projects have 20 years of professional experience, with a single engagement typically lasting around 4.8 years.
The most common languages among freelancers in Germany who have used Machine Translation in their recent projects are German (100%), English (91%), and French (36%).
The most common industries among freelancers in Germany who have used Machine Translation in their recent projects are Information Technology (91%), Automotive (45%), and Manufacturing (45%).
The most common business areas among freelancers in Germany who have used Machine Translation in their recent projects are Information Technology (73%), Product Development (73%), and Research and Development (64%).
Main locations of FRATCH Experts, who have recently used Machine Translation
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 Request a free demo
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Philipp Thomaschewski
FRATCH CEO
