Research Engineer - Reinforcement Learning and Agentic AI (f/m/div.)

Aufgaben

• 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. Profil

• Education:
  • excellent MSc in Computer Science, Machine Learning, Robotics, Systems Engineering, Control, or related fields
  • PhD in Machine Learning, Reinforcement Learning, Agentic AI, Sequential Decision-Making, or related areas
  • strong publication record in leading AI, machine learning, or autonomous systems venues such as NeurIPS, ICLR, ICML, AAAI, IJCAI, CoRL, RSS, AAMAS, or similar
• Experience and Knowledge:
  • Reinforcement Learning & Agentic AI:
    • strong expertise in reinforcement learning, sequential decision-making, or learning-based planning
    • experience with model-based RL, offline RL, hierarchical RL, multi-agent RL, or constrained RL is highly desirable
    • familiarity with agentic AI architectures that involve goal-directed behavior, memory, tool use, multi-step reasoning, and long-horizon task execution
    • ability to design agents that learn from interaction, simulation, or structured feedback in complex environments
  • Systems Engineering & Engineering Intelligence:
    • strong interest in applying AI to systems engineering tasks such as design-space exploration, requirement analysis, architecture optimization, verification support, or engineering workflow automation
    • familiarity with structured engineering artifacts such as requirements, system models, dependency graphs, simulation outputs, or test specifications
    • ability to formulate engineering problems as sequential decision-making or optimization tasks
    • interest in combining formal engineering processes with adaptive AI methods
  • Planning, Simulation & Structured Reasoning:
    • experience with planning, search, optimization, or decision-making under constraints and uncertainty
    • familiarity with simulation-based learning and the creation of training environments for agents operating in technical or cyber-physical domains
    • interest in combining RL with symbolic representations, structured world models, knowledge graphs, or formal methods
    • understanding of how language-based interfaces and reasoning modules can be integrated into agentic decision systems
  • Industrial experience, AI Infrastructure & Experimentation:
    • solid experience in Python and modern deep learning frameworks such as PyTorch, TensorFlow, or JAX in industrial real-world applications
    • familiarity with scalable experimentation, distributed training, and evaluation pipelines
    • experience with Docker, Git, CI/CD, and collaborative software development practices
    • ability to build reproducible research infrastructure for training, benchmarking, and analyzing agentic AI systems
• Personality and Working Practice: You bring a strong scientific track record in top-tier AI venues such as NeurIPS, ICLR, ICML, AAAI, IJCAI, AAMAS, CoRL, or related conferences; you possess entrepreneurial mindset with the ability to translate advanced research into practical innovation; you have strong analytical and conceptual skills, especially in connecting methodological advances to real engineering needs
• Enthusiasm: you feel comfortable working in interdisciplinary teams and contributing to collaborative initiatives across Bosch research and development
• Languages: fluent in English, German is a plus Wissenswertes zum Job

https://www.bosch-ai.com

www.bosch.com/research Please submit all relevant documents (CV, certificates, and links to GitHub or kaggle account).

We offer flexible working models: from various part-time options to mobile working and job sharing. Feel free to contact us.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Work #LikeABosch starts here: Apply now!

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