Agentic AI R&D Intern

Bosch Group • Sunnyvale, CA

2d • $53 - $70

About The Position

The Bosch Research and Technology Center North America (RTC-NA), part of the global Bosch Group, conducts research and develops technologies and systems in areas including artificial intelligence, energy, internet technologies, circuit design, semiconductors, wireless, and advanced MEMS design. The Silicon Valley AI research team focuses on foundation models and generative AI, human-AI collaboration and trustworthy AI, ADAS and autonomous systems, AI systems engineering, and industry AI. The Agentic AI Research Group researches large language models, AI agents, and intelligent autonomous systems, including autonomous and multi-agent systems, reasoning and planning, memory, self-improvement, retrieval-augmented generation, reinforcement learning, and multimodal foundation models.

The intern will conduct Agentic AI research addressing real-world challenges in areas such as Industrial AI, manufacturing, mobility, and smart home. The role offers the opportunity to develop and validate AI technologies on large-scale applications, work with Bosch researchers and domain experts in Silicon Valley and across the global research network, and receive mentorship from AI experts. Research outcomes may be published in leading AI conferences and journals.

Requirements

• 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, machine learning, natural language processing, or a related area.
• Experience developing AI agents using modern frameworks and ecosystems.
• Strong programming skills in Python and experience with modern AI/ML frameworks.
• Familiarity with agent workflows involving 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.

Nice To Haves

• Publication record in top-tier AI, NLP, or machine learning venues such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, or COLM.
• Research experience in LLM-based agents, agent architecture, task planning, reasoning, or autonomous decision-making.
• Research experience in agent optimization, including post-training, reinforcement learning, preference optimization, or self-improvement.
• Experience with retrieval-augmented generation, agentic retrieval systems, or knowledge-grounded agents.
• Experience with multi-agent systems, agent collaboration, or distributed agent workflows.
• Experience with multimodal 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 applying Agentic AI technologies to real-world domains such as engineering design or industrial automation.
• Open-source contributions or demonstrated leadership in AI research communities.

Responsibilities

• Conduct novel research in Agentic AI, LLMs, and related fields.
• Design, implement, and evaluate research prototypes demonstrating innovative AI agent capabilities.
• Collaborate with Bosch researchers and domain experts to address challenging industrial problems.
• Analyze experimental results and identify opportunities for scientific innovation.
• Publish research outcomes in leading AI conferences and journals, such as NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, and COLM.
• Contribute, according to expertise and interests, to research topics including industrial agent systems; autonomous and multi-agent systems; reasoning, planning, and decision-making; agent memory and long-horizon task execution; self-improvement, continual learning, and adaptation; tool use and agent-environment interaction; RAG and Agentic RAG; knowledge integration and grounding; LLM post-training, reinforcement learning, and test-time scaling; multimodal agents; human-agent collaboration; and agent evaluation, reliability, and safety.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern