Research Scientist Agentic AI, Reinforcement Learning and Neuro-Symbolic Systems (f/m/div.)

Research Scientist Agentic AI, Reinforcement Learning and Neuro‑Symbolic Systems (f/m/div.)

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: We grow together, we enjoy our work, and we inspire each other. Welcome to Bosch.

The Robert Bosch GmbH is looking forward to your application!

Responsibilities

• Lead and advance research on intelligent AI systems that can take action, reason over goals and constraints, and organize knowledge through complex neuro‑symbolic structures.
• Focus on next‑generation agentic systems that combine reinforcement learning, structured reasoning, memory, and knowledge‑based representations to operate effectively in semantically rich, technically demanding environments.
• Shape Bosch’s scientific agenda by identifying promising research directions, initiating and coordinating research activities, building connections to external academic and industrial partners, and representing Bosch in relevant research communities.
• Bring a strong external network and effectively position Bosch in collaborative projects, scientific exchanges, and strategic initiatives related to agentic AI, reinforcement learning and neuro‑symbolic systems.
• Develop systems that move from passive understanding toward goal‑directed behavior, integrating reinforcement learning with symbolic abstractions, hierarchical planning, memory, and reasoning.
• Work closely with research scientists, engineers, students, and domain experts across Bosch, mentor students and junior researchers, and contribute to the organizational development of this research area.

Qualifications

Education

• Excellent MSc in Computer Science, Machine Learning, Artificial Intelligence, Robotics, Systems Engineering, or related fields.
• PhD in Machine Learning, Reinforcement Learning, Agentic AI, Neuro‑Symbolic AI, Sequential Decision‑Making, or a closely related area is mandatory.
• Ideally several years of post‑PhD research experience in academia, industry research, or a comparable environment.
• Strong publication record in leading AI and machine learning venues such as NeurIPS, ICLR, ICML, AAAI, IJCAI, CoRL, RSS, AAMAS, ACL, EMNLP, KR, or similar.

Experience and Knowledge

• Agentic AI, RL & Action‑Oriented Systems
  • Strong expertise in reinforcement learning and agentic AI, including sequential decision‑making and learning‑based planning.
  • Experience with advanced RL paradigms such as model‑based, hierarchical, offline, multi‑agent, or constrained RL.
  • Deep understanding of goal‑directed AI systems involving memory, tool use, planning, multi‑step reasoning, and long‑horizon behavior.
  • Ability to design and analyze systems that act in complex environments and improve through interaction, simulation, or structured feedback.
• Neuro‑Symbolic Systems & Knowledge Organization
  • Proven experience in combining learning‑based AI with symbolic or structured representations.
  • Familiarity with neuro‑symbolic architectures, knowledge graphs, formal reasoning structures, and compositional representations.
  • Ability to design systems that organize knowledge in semantically meaningful ways while supporting action, planning, interpretability, and generalization.
• Systems Engineering & Structured Technical Domains
  • Interest in applying advanced AI methods to complex technical and cyber‑physical domains such as systems engineering, robotics, or industrial automation.
  • Experience with structured engineering artifacts (e.g. requirements, system models, simulations, or formal specifications) is an advantage.
  • Ability to frame complex technical challenges in terms of sequential decision‑making, planning, or knowledge‑based reasoning.
• Scientific Leadership, Networking & Mentoring
  • Demonstrated ability to initiate, structure, and lead research activities in a focused technical domain.
  • Strong external scientific network and experience building collaborations with academic and industrial partners.
  • Proven track record in publications, project coordination, and community‑building activities.
  • Mentoring experience with students and junior researchers, combined with strong organizational and coordination skills.
• AI Infrastructure & Research Prototyping
  • Solid experience in Python and modern deep‑learning frameworks (e.g. PyTorch, TensorFlow, JAX).
  • Familiarity with scalable experimentation, reproducible research, and collaborative software development.
  • Ability to translate research ideas into functional prototypes and experimental platforms.
• Scientific Contributions & Mindset
  • Strong sense of ownership and entrepreneurial mindset in driving research topics.
  • Ability to connect fundamental research with long‑term strategic value.
  • Excellent analytical and communication skills, paired with a collaborative, interdisciplinary leadership style.

Personality and Working Practice

Personality and Working Practice: You are a scientifically strong and organizationally capable researcher with a clear ambition to shape and lead research activities in the field of agentic AI at Bosch; you combine deep methodological expertise with external visibility, mentoring experience, and the ability to build and coordinate impactful research efforts.

Languages

Languages: Fluent English skills (written and spoken); German is a plus.

Equal Opportunity

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.