Research Engineer - Training Large Behavior Models with Reinforcement Learning (EG16, f/m/div.)

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As a research engineer in the semantic understanding and reasoning group (CR/AIR4) at Bosch Corporate Research, you will develop next-generation methods for training large behavior models for intelligent cyber-physical systems. Your work will focus on how large-scale AI models can acquire robust, generalizable, and goal-directed behaviors through reinforcement learning, multimodal experience, and interaction with learned or simulated environments. A central part of the role is the use of world models as a foundation for training and validating these systems. In this context, you will investigate how predictive models of environment dynamics, latent state, and agent-environment interaction can support policy learning, planning, behavior synthesis, and evaluation. This includes leveraging world-model-based rollouts for scalable training, using imagined trajectories for efficient policy improvement, and developing validation frameworks that assess generalization, robustness, and safety before real-world deployment. Your work will bridge foundational research and practical implementation, and will contribute to the design of architectures that connect representation learning, latent dynamics modeling, reinforcement learning, and large-scale behavior modeling. Building the infrastructure needed for pretraining, simulation-based learning, fine-tuning, and benchmarking in Bosch-relevant environments is also part of this role. The application space spans a broad range of Bosch domains, including robotics, industrial automation, automated driving, and intelligent building or energy systems. You will collaborate closely with AI researchers, robotics experts, control engineers, and domain specialists to ensure that the developed methods are scientifically strong and strategically relevant for real-world Bosch systems. Your contributions will help establish core Bosch capabilities in scalable behavior learning, model-based reinforcement learning, and physically grounded AI systems that can be trained, validated, and adapted efficiently across applications. Education: excellent MSc in Computer Science, Machine Learning, Robotics, Control, or related technical fields PhD in Machine Learning, Reinforcement Learning, Robotics, Generative AI, or related areas preferred strong publication record in leading AI, machine learning, and robotics venues such as NeurIPS, ICLR, ICML, CoRL, RSS, ICRA, AAAI, IJCAI, or similar Experience and Knowledge: Reinforcement Learning & Behavior Learning expertise in reinforcement learning and sequential decision-making for complex environments experience with model-based, offline, hierarchical, imitation, or constrained RL training large-scale behavior or policy models from multimodal data and interaction designing methods for long-horizon optimization, generalization, and robust adaptation strong interest in behavior validation, robustness testing, sim-to-real, and safety World Models & Predictive Learning solid understanding of world models, latent dynamics, and sequence or generative models using predictive models for imagination-based training, rollouts, and planning experience with latent-state modeling, uncertainty-aware prediction, and validation Interest in connecting data-driven learning with physically grounded reasoning Large Models, Multimodal Learning & Foundation AI experience with large-scale deep learning and transformer-based or multimodal models representation learning across visual, temporal, action, language, or sensor modalities interest in large behavior models as transferable, reusable AI components linking large-model training with policy learning and environment interaction Industrial Experience, Software Engineering & AI Infrastructure strong Python skills and experience with PyTorch, TensorFlow, or JAX experience with simulation platforms (e.g., Isaac Sim, CARLA, MuJoCo, Habitat) familiarity with distributed training, benchmarking, and reproducible pipelines experience with Docker, Git, CI/CD, and multi-GPU or cloud infrastructure Personality and Working Practice: you bring a strong scientific mindset with a proven publication record in top-tier AI and robotics venues; you are able to translate cutting-edge research into practical, value‑creating innovations and connect foundational AI methods with Bosch‑relevant challenges and application scenarios; you have a collaborative mindset and enjoy working across AI research, robotics, control, within engineering teams Languages: fluent English skills written and spoken, German is a plus 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,

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