Research Engineer - Reinforcement Learning and Agentic AI (f/m/div.) / REF283700N

Research Engineer – Reinforcement Learning and Agentic AI (f/m/div.) / REF N

Job Description

• Develop the next generation of agentic AI systems based on reinforcement learning, focusing on applications in systems engineering.
• Design AI agents that interact with engineering artifacts, reason over goals and constraints, and improve their behavior through feedback, simulation, and optimization.
• Integrate advanced RL methods with the realities of Bosch engineering environments, including representation of states and actions, symbolic knowledge, reward mechanisms, and simulation or surrogate environments.
• Collaborate with research scientists, AI engineers, and systems engineering experts to prototype and evaluate these methods in realistic use cases.

Qualifications

• 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: 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 and 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, and 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 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: strong scientific track record in top‑tier AI venues, entrepreneurial mindset, and strong analytical and conceptual skills
• Enthusiasm for interdisciplinary teamwork and collaborative initiatives across Bosch research and development
• Languages: fluent in English, German is a plus

Additional Information

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.