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Agentic AI R&D Intern

Robert Bosch Ltd.
Sunnyvale, USA
Internship
Intern
Onsite
Discovered 1 weeks ago
python

Full Job Posting

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
  • 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, or industrial automation.
  • Open-source contributions or demonstrated leadership in AI research communities.

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, or industrial automation.
  • Open-source contributions or demonstrated leadership in AI research communities.
  • Equal Opportunity Employer, including disability / veterans
  • Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.
  • BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics)
  • FIRST Robotics (For Inspiration and Recognition of Science and Technology)
  • AWIM (A World In Motion)
  • The U.S. base salary range for this intern position is $53.00-70.00 hourly. Within the range, individual pay is determined based on several factors, including, but not limited to, type of degree, work experience and job knowledge, complexity of the role, type of position, job location, etc. Your Hiring Manager can share more details about the specific salary range for this position during the interview process.
  • For more information on our culture and benefits, please visit:
  • Culture and Benefits | Bosch in the USA
  • All your information will be kept confidential according to EEO guidelines.
  • The Bosch Research and Technology Center North America with offices in Sunnyvale, California, Pittsburgh, Pennsylvania, and Cambridge, Massachusetts is a part of the global Bosch Group (www.bosch.com), a company with over 70 billion euro revenue, 400,000 employees worldwide, a very diverse product portfolio, and a history spanning over 125 years. The Research and Technology Center North America (RTC-NA) is dedicated to providing technologies and system solutions for various Bosch business fields, primarily in the field of artificial intelligence, energy technologies, internet technologies, circuit design, semiconductors and wireless, as well as advanced MEMS design.
  • As part of the global research organization, our AI research in Silicon Valley focuses on Foundation Models and Gen AI, Human-AI Collaboration and Trustworthy AI, AI for Advanced Driver-Assistance Systems (ADAS) and Autonomous Systems, AI Systems Engineering, and Industry AI. We develop scalable, intelligent, and trustworthy AI solutions to enable inspiring user experiences for Bosch products and services in application areas such as ADAS, smart manufacturing, enterprise AI, healthcare, smart home, and building solutions.
  • Originating from Bosch AI research in Silicon Valley, our Agentic AI Research Group advances cutting-edge research in large language models (LLMs), AI agents, and intelligent autonomous systems. Our research spans autonomous and multi-agent systems, agent reasoning and planning, agent memory, self-improvement, retrieval-augmented generation (RAG), reinforcement learning, and multimodal foundation models. We aim to develop novel algorithms and systems that enable AI agents to reason, learn, collaborate, and act effectively in complex real-world environments. We actively collaborate with leading researchers and organizations across academia and industry to advance the state of the art in Agentic AI. Our work regularly contributes to internationally renowned conferences and journals, including NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, COLM, and TMLR.

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