Agentic AI R&D Intern

You will work on cutting-edge Agentic AI research while tackling real-world challenges in domains such as Industrial AI, Manufacturing, Mobility, and Smart Home. Unlike purely academic environments, you will have the opportunity to develop and validate novel AI technologies on large-scale real-world applications with measurable impact. You will work closely with experienced Bosch researchers in Silicon Valley and across our global research network, receive mentorship from leading AI experts, and help transform cutting-edge research into real-world AI systems deployed in industrial domains. Responsibilities Depending on your expertise and interests, you will contribute to one or more cutting-edge research topics, including: • Agent systems for industrial applications • Autonomous AI agents and multi-agent systems • Agent reasoning, planning, and decision making • Agent memory and long-horizon task execution • Agent self-improvement, continual learning, and adaptation • Tool use, function calling, and agent-environment interaction • Retrieval-Augmented Generation (RAG) and Agentic RAG • Knowledge integration and grounding for agents • LLM post-training, reinforcement learning, and test-time scaling • Multi-modal agents leveraging language, vision, and structured data • Human-agent collaboration and interactive AI systems • Agent evaluation, benchmarking, reliability, and safety Key responsibilities include: • Conduct novel research in Agentic AI, LLMs, and related fields. • Design, implement, and evaluate research prototypes demonstrating innovative AI agent capabilities. • Collaborate closely with Bosch researchers and domain experts to address challenging industrial problems. • Analyze experimental results and identify opportunities for scientific innovation. • Publish research outcomes in top-tier AI conferences and journals, such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, and COLM. Basic Qualifications • Currently enrolled in a Ph.D. or Master's program in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, or a related field. • Strong research background in Agentic AI, Large Language Models (LLMs), machine learning, natural language processing, or related areas. • Experience developing AI agents using modern frameworks and ecosystems • Strong programming skills in Python and experience with modern AI/ML framework • Familiarity with agent workflows involves tool use, function calling, retrieval, memory, and multi-step reasoning. • Experience conducting independent research, designing experiments, and analyzing results. • Strong written and verbal communication skills and ability to work effectively in a collaborative research environment. • Minimum GPA of 3.0 Preferred Qualifications • Publication record in top-tier AI, NLP, or machine learning venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, COLM). • Research experience in LLM-based agents, agent architecture, task planning, reasoning, and autonomous decision making. • Research experience in agent optimization, including post-training, reinforcement learning, preference optimization, and self-improvement. • Experience with retrieval-augmented generation (RAG), agentic retrieval systems, and knowledge-grounded agents. • Experience with multi-agent systems, agent collaboration, and distributed agent workflows. • Experience with multi-modal foundation models involving language, vision, and structured data. • Experience in reinforcement learning, online learning, or adaptive decision-making systems. • Experience with agent benchmarking, evaluation methodologies, reliability, safety, or alignment. • Experience in applying Agentic AI technologies to real-world domains such as engineering design