As a research engineer in the semantic understanding and reasoning group (CR/AIR4) at Bosch Corporate Research, you will develop the next generation of agentic AI systems based on reinforcement learning, with a primary focus on applications in systems engineering. Your work will address how intelligent agents can support and partially automate complex engineering workflows by learning to make structured decisions in environments shaped by constraints, specifications, system models, and long-horizon objectives.
This role centers on the design of AI agents that do not merely respond to prompts, but can interact with engineering artifacts, reason over goals and constraints, and improve their behavior through feedback, simulation, and optimization. You will investigate how reinforcement learning, hierarchical decision-making, model-based methods, and planning can be combined with modern agentic AI architectures to support engineering tasks such as architecture exploration, requirement analysis, system-level trade-off evaluation, validation support, and process optimization.
A core part of the role is to connect advanced RL methods with the realities of Bosch engineering environments. This includes defining suitable state and action representations for technical workflows, integrating symbolic and structured knowledge, designing reward mechanisms aligned with engineering objectives, and building simulation or surrogate environments in which agents can learn safely and efficiently. Your work may also involve the interaction between language-based agents and formal engineering tools, enabling AI systems that can operate across textual, symbolic, and numerical representations.
You will work closely with research scientists, AI engineers, and systems engineering experts across Bosch to prototype and evaluate these methods in realistic use cases. Your contributions will help shape Bosch's long-term capabilities in intelligent engineering support systems and agent-based automation for complex technical domains.