AI Jobs in Munich

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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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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 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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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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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 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 Product Manager, you will define and drive the AI product roadmap ensuring alignment with business objectives and user needs. You will collaborate with cross-functional teams including engineering, design, and marketing to develop and launch AI-powered features. Responsibilities include conducting market research and analyzing user feedback to identify opportunities for AI integration, working closely with data scientists and machine learning engineers to optimize AI models for accuracy, performance, and user impact, defining key performance indicators to measure success and iterating based on data-driven insights, staying up to date with AI trends, emerging technologies, and best practices, and ensuring ethical AI usage and compliance with data privacy regulations. This role is fully onsite in Lisbon, involving close in-person collaboration in a dynamic and fast-paced environment.

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Lead and grow a team of Forward Deployed Engineers (FDE) delivering production systems with frontier models. Own end-to-end delivery outcomes through clarity, speed, tight coordination, and technical quality. Codify effective practices into tools, playbooks, and roadmap inputs to create leverage for OpenAI and the wider developer community. Identify and raise early indicators with urgency from product behavior, customer environments, or delivery practices. Use judgment to decide when action is required. Set high performance standards for FDEs and support each individual's growth through direct, actionable feedback. Define staffing and support strategies for scalable field teams without added complexity.

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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.

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Lead the research and development of novel deep learning algorithms that enable robots to perform complex, contact-rich manipulation tasks. Explore the intersection of computer vision and robotic control, designing systems that allow robots to perceive and interact with objects in dynamic environments. Create models that integrate visual data to guide physical manipulation, moving beyond simple grasping to sophisticated handling of diverse items. Collaborate with a multidisciplinary team of engineers and researchers to translate cutting-edge concepts into robust capabilities deployable on physical hardware for industrial applications. Research and develop deep learning architectures for visual perception and sensorimotor control in contact-rich scenarios. Design algorithms enabling robots to manipulate complex or deformable objects with high precision. Collaborate with software engineers to optimize and deploy research prototypes onto physical robotic hardware. Evaluate model performance in simulation and real-world environments to ensure robustness and reliability. Identify opportunities to apply state-of-the-art advancements in computer vision and robot learning to practical industrial problems. Mentor junior researchers and contribute to the technical direction of the manipulation research roadmap.

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The Partner AI Deployment Engineer (P-ADE) leads technical delivery with OpenAI partners across EMEA to scale customer deployments built on the OpenAI platform. Responsibilities include acting as a primary technical delivery partner for OpenAI partners, supporting customer deployments across multiple industries and use cases, working with partner delivery teams and customer stakeholders to translate solution designs into deployable, production-ready architectures, supporting customer time to value through hands-on prototyping, integration support, architectural guidance, and troubleshooting during critical delivery phases. The role involves close collaboration with Solutions Engineers, Forward Deployed Engineers, and other ADEs to ensure appropriate technical expertise is engaged from design through production rollout. The engineer helps partners operationalize solutions by addressing scalability, reliability, security, and safety considerations for enterprise production environments, contributes to reusable deployment patterns, reference architectures, and delivery guidance for repeatable execution, acts as a technical quality and governance point to ensure solutions meet OpenAI's standards before and after go-live, and captures and synthesizes feedback from deployments to share insights with Applied, Research, and partner teams to improve delivery playbooks and platform capabilities.