AI Research Engineer – Agentic AI

Bosch Group•Sunnyvale, CA

2d•Onsite

About The Position

The Bosch Research and Technology Center North America (RTC-NA) is part of the global Bosch Group and focuses on providing technologies and system solutions in areas like artificial intelligence, energy technologies, and internet technologies. Our AI research in Silicon Valley concentrates 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 aim to develop scalable, intelligent, and trustworthy AI solutions for various Bosch products and services. The Vision and Language AI Group specifically advances research in large language models (LLMs) and 3D computer vision. Our LLM work includes AI agents, retrieval-augmented generation, and adapting LLMs for Bosch applications. Our 3D vision research focuses on spatial intelligence with 3D world models for embodied control and sim-to-real transfer. We integrate these areas to build intelligent systems that can reason and communicate across language and spatial environments. We also collaborate with academic and industry leaders, publishing in renowned conferences and journals.

Requirements

Bachelor or master's degree in computer science or engineering
Strong software engineering skills in Python
Hands-on experience with LLMs and agentic systems, such as tool-using agents, planner-executor patterns, multistep reasoning pipelines, RAG systems
Solid understanding of ML fundamentals and practical model usage, including prompting, evaluation, and error analysis
Ability to design experiments and interpret results, including ablations, statistical thinking, clear success criteria and measurable KPIs
Strong communication and documentation skills including clear write-ups, reproducible experiments, and crisp technical presentations

Nice To Haves

3+ years experiences in industrial research.
Experience with one or more of the following topics: Agent frameworks, Structured generation, Agent memory management, Self-improving agents, LLM fine-tuning & reinforcement learning, Model optimization for edge, Harness engineering
Hands-on experience on production of AI systems

Responsibilities

Design, build, and evaluate agentic AI systems that can plan, reason, act, and collaborate across tools and environments, including single- and multi-agent setups for complex, long-horizon tasks.
Develop robust agent harnesses and evaluation frameworks covering end-to-end testing, regression analysis, trace logging, replayability, and metrics for success, cost, latency, robustness, and safety.
Implement self-improving agent loops using reflection, critique, self-debugging, and iterative optimization strategies driven by agent experience, execution traces, and automated feedback.
Architect and optimize agent memory systems, including short-term and long-term memory, retrieval-augmented generation, summarization, compression, forgetting policies, and privacy-aware retention.
Enable reliable deployment of agents on constrained and edge environments, focusing on model/runtime optimization, partial or offline execution, secure tool-use, and seamless edge-cloud coordination.