AI Jobs in Germany

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Contributors may design graduate- and industry-level mechanical engineering problems grounded in real practice, evaluate AI-generated solutions for correctness, assumptions, and engineering logic, validate analytical or numerical results using Python (NumPy, SciPy, Pandas), improve AI reasoning to align with first principles and accepted engineering standards, and apply structured scoring criteria to assess multi-step problem solving.

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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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Lead technical delivery with OpenAI partners across EMEA, supporting customer deployments built on the OpenAI platform for a variety of industries and use cases. Work with partner delivery teams and customer stakeholders to translate solution designs into deployable, production-ready architectures. Support customer time to value through hands-on prototyping, integration support, architectural guidance, and troubleshooting. Collaborate with Solutions Engineers, Forward Deployed Engineers, and other ADEs to engage appropriate technical expertise from design through to production rollout. Help partners operationalize solutions addressing scalability, reliability, security, and safety for enterprise production. Contribute to reusable deployment patterns, reference architectures, and delivery guidance to enable repeatable execution. Act as a technical quality and governance point during deployments ensuring solutions meet OpenAI's standards and best practices. Capture and synthesize feedback from customer deployments to improve delivery playbooks and platform capabilities through collaboration with Applied, Research, and partner teams.

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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 specify future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingestion. The engineer will work at the intersection of NoC, performance modeling, and RTL design to guide architectural decisions, collaborate across hardware, software, and systems teams to define and refine networking requirements, and help drive forward next-generation CPU networking architecture specifically for AI/ML workloads.

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The role involves defining and building next-generation CPU networking architecture for datacenter and emerging robotics/automotive applications. Responsibilities include 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. The position requires 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 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 RL methodology, maintain a high-quality training code-base, and conduct large-scale experiments to hill-climb performance benchmarks. You will conduct large-scale LLM training runs, analyze evaluation scores in depth, propose hypotheses for improvement, and directly implement them to maximize performance on benchmarks. You will identify, implement, and iterate on novel approaches to multi-turn reinforcement learning, optimize RL training loops for large-scale training by identifying bottlenecks, and collaborate cross-functionally to turn raw feedback into actionable training signals to ensure RL iterations lead to measurable improvements in downstream performance.

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

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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. Leverage large language models (LLMs) like Roo Code and Claude to accelerate development cycles, automate repetitive tasks, and improve overall code quality.

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Contribute to the development of next-generation CPU networking architecture for both datacenter and emerging robotics/automotive applications, including current datacenter networking efforts and early-stage automotive/robotics device specifications. Work at the intersection of Network-on-Chip (NoC), performance modeling, and RTL design to guide architectural decisions. Collaborate across hardware, software, and systems teams to define and refine networking requirements. Drive forward next-generation CPU networking architecture for AI/ML workloads, and take an early-stage concept from seeding and specification through to project initiation in automotive/robotics networking.

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The role involves defining and building next-generation CPU networking architecture for both datacenter and emerging robotics/automotive applications. The engineer will contribute to current datacenter networking efforts while also helping to seed and specify future medium- to low-power robotics/automotive devices for AI/ML compute and sensor ingest. They will work at the intersection of NoC, performance modeling, and RTL design to guide architectural decisions and collaborate across hardware, software, and systems teams to define and refine networking requirements. The position includes driving the development of 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.