AI Jobs in Germany

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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.

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The role involves defining and building next-generation CPU networking architecture for datacenter and emerging robotics/automotive applications. The engineer will contribute to current datacenter networking efforts while helping to seed and specify future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingestion, with an initial focus on datacenter networking and robotics in the automotive/robotics space. The position requires working at the intersection of Network on Chip (NoC) design, performance modeling, and RTL design to guide architectural decisions and collaborating across hardware, software, and systems teams to define and refine networking requirements. The responsibilities also include driving forward next-generation CPU networking architecture for AI/ML workloads and taking early-stage automotive/robotics networking concepts from seeding and specification through to project initiation.

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The role involves defining and building next-generation CPU networking architecture for datacenter and emerging robotics/automotive applications, contributing to current datacenter networking efforts, and helping to seed and specify future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingest. Responsibilities also include working at the intersection of Network-on-Chip (NoC), performance modeling, and RTL design to guide architectural decisions, collaborating across hardware, software, and systems teams to define and refine networking requirements, and driving forward next-generation CPU networking architecture for AI/ML workloads.

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As a senior AI Researcher for reinforcement learning, you will shape and improve the underlying reinforcement learning methodology, maintain a high-quality training codebase, and conduct large-scale experiments to improve performance benchmarks. Your responsibilities include conducting large-scale LLM training runs, analyzing evaluation scores, proposing and implementing improvements, staying at the forefront of reinforcement learning research by identifying and iterating on novel approaches, optimizing RL training loops to scale training infrastructure, and collaborating cross-functionally with other post-training teams to convert feedback into actionable training signals for measurable improvements in performance.

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The Field Application Engineer, Automotive Robotics will contribute to defining and building next-generation CPU networking architecture for datacenter and emerging robotics/automotive applications, including both datacenter networking efforts and early-stage automotive/robotics scoping and specifications. Responsibilities include working at the intersection of NoC, performance modeling, and RTL design to guide architectural decisions, collaborating across hardware, software, and systems teams to define and refine networking requirements, and helping to drive forward next-generation CPU networking architecture for AI/ML workloads. The role involves taking an early-stage concept within automotive/robotics networking from seeding and specification through to project initiation.

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The job involves defining and building next-generation CPU networking architecture for both datacenter and emerging robotics/automotive applications. Responsibilities include contributing to current datacenter networking efforts and helping to specify future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingest, with an initial focus on datacenter networking and robotics within the automotive/robotics space. The role requires working at the intersection of Network on Chip (NoC), performance modeling, and RTL design to guide architectural decisions, collaborating across hardware, software, and systems teams to define and refine networking requirements, and helping to drive forward next-generation CPU networking architecture for AI/ML workloads. Additionally, the position involves taking early-stage automotive/robotics networking concepts from seeding and specification through to project initiation.

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As an Automotive and Robotics SoC Architect at Tenstorrent, you will define scalable, top-down system architectures that unify the company's CPU and AI technologies for next-generation automotive applications. This role involves shaping the architectural direction of automotive and robotics products to meet high standards for performance, safety, reliability, and security. The position requires strong technical leadership, systems thinking, and cross-functional collaboration, and is central to delivering world-class automotive solutions.

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Engineer the systems required to train foundation models at scale to maximize hardware utilization and training throughput on large-scale GPU clusters. Profile training loops using PyTorch Profiler, Nsight Systems and Nsight Compute to identify system- and kernel-level bottlenecks and maximize model throughput. Configure and tune composite parallelism strategies such as tensor parallelism (TP), data parallelism (DP), hybrid sharded data parallel (HSDP/FSDP), and expert parallelism (EP) to optimize load balance, minimize critical-path bottlenecks, and manage communication-to-computation trade-offs for large-scale large language model (LLM) training. Collaborate with AI Researchers to define model architectures that enhance hardware efficiency without compromising convergence.

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Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. Partner directly with customers to design and implement AI-enhanced development workflows from rapid prototyping through scalable production rollout. Build high-quality demos, reference implementations, and workflow automations using Codex itself as part of the development process. Lead large-format workshops, technical deep dives, and hands-on enablement sessions to help engineering organizations adopt AI coding tools effectively and safely. Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community. Gather high-fidelity product insights from real customer deployments and translate them into product proposals and model feedback for internal teams. Influence customer strategy and decision-making by framing how AI coding tools fit into their SDLC, technical roadmap, and organizational workflows. Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.