About the job
At Bosch, we are at the forefront of innovation, shaping the future of intelligent systems that are efficient, safe, and enhance lives globally. We are seeking a visionary research engineer to join our dynamic team, focusing on the powerful intersection of reinforcement learning (RL) and agentic AI. Your groundbreaking work will directly impact critical Bosch domains, including automated driving, smart home systems and energy management, advanced and flexible manufacturing. As a key contributor, you will redefine the state-of-the-art in AI capabilities, driving foundational research and building core systems that directly shape Bosch's product portfolio. Join us in transforming complex challenges into real-world impact.
Responsibilities
Architect, develop, and deploy sophisticated AI systems that seamlessly integrate reinforcement learning with agentic AI systems and multi-modal foundation models.
Train and fine-tune multi-modal large models to precisely align their behaviors with Bosch product requirements and use cases.
Advance AI paradigms by enhancing existing machine learning systems, integrating cutting-edge data-driven, generative approaches with robust, safe, and scalable reinforcement learning algorithms.
Conduct original research, leading high-impact projects to solve challenging scientific and applied problems at the confluence of RL and agentic AI, making a real impact on Bosch products.
Partner closely with internal customers and product teams to deeply understand requirements, conceptualize innovative solutions, and deliver high-quality Minimum Viable Products (MVPs).
Publish and present groundbreaking research findings in premier academic venues and actively contribute to the broader scientific community.
Qualifications
Minimum
Excellent PhD in Computer Science, Artificial Intelligence, Machine Learning or a related field, with a strong publication record in leading conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ACL).
Comprehensive practical experience and theoretical understanding in Deep Reinforcement Learning (RL) and behavioral learning, ideally with expertise in emerging behaviors with RL, Safe RL, Offline RL, and/or Multi-agent RL.
Proven track record with Agentic AI, Large Language Models (LLMs), Visual Language Models (VLMs), Visual Language Action Models (VLAs) including practical experience with advanced fine-tuning methods such as Supervised Fine-Tuning, DPO, and RLHF.
Excellent knowledge of Python and profound knowledge of industry-standard Machine Learning frameworks and libraries (e.g., PyTorch, Hugging Face, JAX, TensorFlow); good knowledge of C++ and related frameworks (e.g., ROS 2).
Experience in software development in larger teams and the deployment of ML-based systems for real-world solutions, especially background knowledge in MLOps and CI/CD is welcome.
Preferred
German language skills are a plus.
Background knowledge in MLOps and CI/CD is welcome.