AI Jobs in Berlin

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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.

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You will be responsible for defining operational domains and evaluating the reliability of the AI capabilities developed in-house. You will develop and extend the state-of-the-art in uncertainty quantification and uncertainty calibration. This involves understanding the AI systems built, interfacing with them, and evaluating their robustness in real-world and adversarial scenarios. You will contribute to impactful projects and collaborate with people across several teams and backgrounds.

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The AI Engineering Manager is responsible for building and leading an engineering team focused on AI agent and workflow orchestration systems within n8n. The role includes leading, coaching, and developing engineers working on agent-based systems, workflow orchestration, and AI-powered nodes; fostering a culture of experimentation and rapid iteration; helping the team design reliable, composable agentic workflows; supporting hiring and team shaping with an emphasis on AI engineering and distributed systems thinking. The manager creates clarity in a fast-evolving AI landscape by translating evolving AI capabilities into clear product and engineering direction, guiding architectural decisions, and focusing on delivering reliable value to users. Execution duties include partnering with Product and Design to define and ship AI workflow experiences, ensuring delivery of end-to-end agent systems, establishing best practices for workflow reliability, and improving user workflows' building, testing, and debugging. The role demands technical leadership in agent systems by mentoring the team on LLM integrations, agent frameworks, and workflow orchestration, supporting architecture decisions, stepping into technical details to unblock the team, and promoting best practices for safety and predictability in agent-driven systems.

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Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.

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As an AI Trainer - Junior Journalist, your responsibilities include crafting original, clear, and fact-checked responses based on project guidelines, generating prompts that challenge AI, defining comprehensive scoring criteria to evaluate the accuracy of AI's answers, and following style and quality standards to ensure consistency. You will help train AI models by shaping how they understand and generate human-like text, focusing on reasoning, logic accuracy, nuance, and clarity. The work is project-based and non-permanent, involving tasks estimated to require around 10–20 hours per week during active phases of the project.

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Contributors may design original computational STEM problems that simulate real scientific workflows, create problems requiring Python programming to solve, ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains and creative problem-solving approaches, verify solutions using Python with standard libraries such as Numpy, Pandas, Scipy, and scikit-learn, and document problem statements clearly while providing verified correct answers.

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Build and ship backend features for Taktile's AI agent platform in Python (FastAPI) deployed on AWS serverless infrastructure. Own work end-to-end by collaborating on scope, implementation, testing, releasing, and iterating based on real usage and customer feedback. Improve how agents run, connect to external tools, and behave when errors occur. Use AI tools daily to move faster, improve quality, and build AI-native capabilities. Review pull requests in depth, improve test coverage and CI/CD, and raise engineering excellence and reliability. Engage in team rituals including daily syncs, planning sessions, demos, and technical deep-dives. Grow professionally by learning from experienced team members, contributing to foundational product layers, and participating in cross-team learning groups. Participate in daytime operations duty and join an on-call rotation to support system ownership and DevSecOps skill growth.

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Contributors may design rigorous electrical engineering problems reflecting professional practice, evaluate AI solutions for correctness, assumptions, and constraints, validate calculations or simulations using Python (NumPy, Pandas, SciPy), improve AI reasoning to align with industry-standard logic, and apply structured scoring criteria to multi-step problems.

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Contributors may design rigorous energy engineering problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.

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Contributors may design rigorous electrical engineering problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.