AI Jobs in Germany

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Design original computational biology problems that simulate real biology research workflows; create problems requiring Python programming to solve (using Numpy, SciPy, BioPython); ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks); develop problems requiring non-trivial reasoning chains in bioinformatics, systems biology, and molecular modeling; base problems on real research challenges or practical applications from biology practice; verify solutions using Python with standard computational biology libraries; document problem statements clearly and provide verified correct answers.

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Contributors may design original computational biology problems simulating real biology research workflows; create problems requiring Python programming using libraries such as Numpy, SciPy, and BioPython; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning in bioinformatics, systems biology, and molecular modeling; base problems on real research challenges or practical applications from biology; verify solutions using Python with standard computational biology libraries; document problem statements clearly and provide verified correct answers.

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The Director of Engineering will lead and scale n8n's Core Experience engineering organization, managing multiple engineering teams across Core Experience, including teams focused on AI, Nodes, Community, and Adoption & Retention. Responsibilities include designing the organization for scale, fostering a high-performance culture, setting technical direction, partnering closely with Product, Design, and Engineering leadership to translate strategy into clear priorities and execution, and ensuring teams deliver meaningful outcomes with a balance of speed, quality, and maintainability. The role also involves supporting and guiding teams building AI-powered product experiences, helping shape AI-native product development, creating conditions for effective team operation through growth and changing priorities, coaching leaders and teams for strong execution and collaboration, and representing the Core Experience area within the broader engineering leadership team to align its direction with company goals.

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Contributors design original computational mathematics problems that simulate real mathematical research workflows; create problems requiring Python programming to solve using libraries like Numpy, SciPy, and Sympy; ensure problems are computationally intensive and not solvable manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in areas such as number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications; verify solutions using Python with standard mathematical libraries; and document problem statements clearly with verified correct answers.

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Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.

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Define and drive the AI product roadmap, ensuring alignment with business objectives and user needs. Collaborate with cross-functional teams, including engineering, design, and marketing, to develop and launch AI-powered features. Conduct market research and analyze user feedback to identify opportunities for AI integration. Work closely with data scientists and machine learning engineers to optimize AI models for accuracy, performance, and user impact. Define key performance indicators (KPIs) to measure success and iterate based on data-driven insights. Stay up to date with AI trends, emerging technologies, and best practices to ensure products remain competitive. Ensure ethical AI usage and compliance with data privacy regulations.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into the applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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As an AI Workflow Engineer on the Tendem project, responsibilities include designing and evaluating workflows integrating LLM APIs such as OpenAI, Claude, Gemini, or similar; building and optimizing AI-powered automation systems for content generation, enrichment, and communication; developing and refining prompt frameworks to ensure consistent, high-quality outputs; creating personalized automated outreach systems involving email, messaging flows, and lead enrichment; structuring and preprocessing data to maintain stable model behavior and reliable outputs; and analyzing and improving AI-driven pipelines for scalability, efficiency, and accuracy.

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Design, build, and evaluate advanced workflows in self-hosted n8n environments. Architect multi-system integrations for scalable automation pipelines. Develop and optimize AI-powered workflows such as content generation, automation pipelines, and enrichment systems. Build and maintain lead generation, outreach, and data processing automation systems. Implement web scraping workflows and ensure reliable data extraction and processing. Optimize workflow execution, node sequencing, and error handling to prevent failures, delays, and API timeouts.

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Contributors may design rigorous mathematics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.