AI Jobs in Berlin

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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 will involve understanding the AI systems built by the company, 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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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.

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Design and implement atomistic simulation workflows for polymer systems, including polymerisation and melt equilibration through to production runs. Implement complementary simulation techniques beyond molecular dynamics (MD) to address complex industry problems. Collaborate with AI research teams to integrate machine learning models into atomistic simulation workflows. Work closely with experimental partners to validate and ensure simulation outputs align with real lab measurements and trends are reproducible and explainable. Translate materials challenges from partners into simulation strategies and deliver clear, rigorous findings suited for industrial collaborations. Serve as an internal expert on polymer science to guide AI researchers on physical constraints and material behaviours. Collaborate across teams of machine learning researchers, computational chemists, and experimentalists contributing independently and leveraging complementary expertise. Contribute to the development of CuspAI's core infrastructure and roadmap for multi-scale materials discovery.

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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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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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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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Design, build, and operate AI agents that power critical parts of Langdock's operations including customer support automation and internal workflows. Own production agents end-to-end by designing, deploying, monitoring, and improving the AI agents that handle real customer interactions and internal workflows. Investigate and fix issues when agents misbehave by digging into logs to identify root causes. Manage agent reliability, cost, and quality by tracking performance, costs, failures, and reasons; set budgets, tune behavior, adjust governance rules, and ensure agents perform effectively. Design the orchestration layer to define team structures, set approval gates, and build operational infrastructure for safe and scalable autonomous agent work. Integrate agents with internal and external APIs to provide context and capabilities. Constantly experiment with new models, techniques, and use cases by benchmarking and shipping improvements.

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Create functional black box tests for large codebases in various source languages, create and manage Docker environments to ensure 100% reproducible builds and test execution across different platforms, monitor code coverage and configure automated scoring criteria to meet industry benchmark-level standards, and leverage LLMs such as Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.