AI Jobs in Germany

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.

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As the AI Enablement Engineer at voize, you will define, operationalise, and scale how artificial intelligence is adopted within the company. You will build the AI infrastructure by designing and maintaining voize's internal AI productivity suite, connecting agents, data, and workflows to enable cross-functional automation and decision-making. You will enable and educate the organisation by building AI fluency through onboarding sessions, workshops, playbooks, and function-specific best practices. You will set guardrails by establishing and enforcing standards for AI governance, safe use, data handling, and compliance in partnership with Security and IT. You will consult and collaborate with teams and leadership to embed AI into workflows, build agents and automations, and translate business needs into prioritised AI initiatives. You will drive change by managing the transition towards an organisation where humans and AI agents work side by side. You will analyze tool usage and adoption signals, spot fatigue patterns, and iterate on improvements that change outcomes. Additionally, you will help create and maintain a language model-readable context layer with a sensible update process so agents and humans operate on shared truth.

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Design computational material science problems to challenge a frontier AI model requiring specialized tools. Pick an anchor tool and design a problem based on its waveform-processing kernels, geophysical inversion routines, sub-surface flow solvers, or data pipelines. Write a Python reference solution, supply input files and model or domain definitions where needed. Decide the numerical answer and the tolerance for correctness. Test the problem against the AI model in batches of parallel attempts, tuning difficulty until the agent succeeds in a small number of attempts. Submit tasks for senior reviewer feedback to ensure quality. Tune problems iteratively based on AI performance to achieve a 10–30% pass rate, rewriting scenarios and adjusting parameters as needed while gaining expertise in both the tool and AI behavior.

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Lead, hire, and mentor a team of AI Deployment Engineers supporting Codex customers across strategic accounts; own the operating model and engagement strategy for Codex deployment efforts to ensure customers move from pilot to production adoption; guide teams in designing and implementing AI-enhanced development workflows, automations, and scalable deployment architectures; act as the senior technical escalation point for complex customer implementations and deployment challenges; partner with Sales, Product, Research, and Applied Engineering teams to align customer outcomes with product direction and roadmap priorities; help establish repeatable deployment playbooks, technical patterns, and best practices for scaled adoption of AI coding tools; coach engineers to serve as trusted advisors to engineering leadership and executive stakeholders; synthesize insights from customer deployments into actionable feedback for internal teams; champion safe, reliable, and effective adoption of AI-powered development workflows across industries.

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Design computational engineering problems to challenge a frontier AI model using specialized tools like Cantera, CoolProp, CalculiX, OpenFAST, or others installed in a sealed Linux container. Write Python reference solutions and supply necessary input files and definitions. Determine the numerical answer and appropriate domain-specific tolerance for correctness. Test and tune the problem difficulty against batches of parallel model attempts to achieve a pass rate between 10-30%. Submit tasks for review by a senior expert for feedback and quality assurance. Continuously refine problems by rewriting thermodynamic cycles, adjusting material models and boundary conditions, and analyzing model behavior through test attempts. Gain a deeper understanding of both the engineering tools and the AI model's approach to complex thermal, structural, and fluid mechanics problems.

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Build realistic consulting project environments by creating detailed project scenarios grounded in real engagement dynamics, including industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents by breaking projects into discrete tasks that mirror real consulting work such as market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task, which are used to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This role is remote, project-based, and focused on analytical design and evaluation as an individual contributor.

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AI Product Builders at n8n own one focused AI workstream where they are close to users, technology, and the future of AI products. They work either independently or alongside a senior PM as a mentor and collaborate closely with cross-functional teams including Engineering, Design, Data, Product Marketing, DevRel, Community, Support, and Customer Success. The role is not focused on ML platform or research but involves building with AI deeply through products, prototypes, public projects, agent workflows, or tools used by real users. Possible workstreams include AI TRUST, which focuses on improving AI quality, observability, reliability, and human-in-the-loop patterns to make AI work in production, and AI BUILDING / SUPER AGENT, which focuses on making n8n an AI-first product by shifting usage to AI-native interfaces and features such as Super Agent, custom memory, Skills, and proactive suggestions.

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Work directly with strategic startup customers to understand critical workflows, uncover failure modes, and identify high-impact opportunities for improvement. Prototype and iterate on prompts, agents, and workflow designs to better understand system behavior and unlock customer value. Synthesize and deliver valuable feedback to the Product and Research teams, turning real usage patterns into clear, reproducible evaluations, benchmarks, and technical artifacts that improve model and product quality and ensure customer-grounded learnings influence roadmap and model development. Build repeatable tools, patterns, and evaluation approaches that raise the quality bar across multiple use cases. Operate with strong judgment in ambiguous environments, balancing immediate technical problem-solving with longer-term system improvement. Build relationships within the startup ecosystem, serving as a technical partner to both individual customers and the broader community.

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Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.

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As an n8n Workflow Developer on the Tendem project, the responsibilities include designing, building, and evaluating advanced workflows in self-hosted n8n environments, architecting multi-system integrations for scalable automation pipelines, developing and optimizing AI-powered workflows such as content generation and enrichment systems, building and maintaining lead generation, outreach, and data processing automation systems, implementing web scraping workflows, and ensuring reliable data extraction and processing. Additionally, the role involves optimizing workflow execution, node sequencing, and error handling to prevent failures, delays, and API timeouts, as well as collaborating with Tendem Agents to provide structured thinking, automation expertise, and quality control for efficient and reliable workflow execution.