AI Research Engineer – Agentic AI

Bosch USA; Sunnyvale (CA); On-site; USD 165,000 - 180,000; Full time

Benefits offered by this job

Premium health coverage

401(k) with generous matching

Financial planning resources

Ample paid time off

Parental leave

Life and disability protection

Job summary

Bosch USA in Sunnyvale, CA, seeks an experienced AI researcher to design, build, and evaluate agentic systems for long-horizon tasks. You will work on memory architectures, edge deployment, and robust evaluation frameworks with a focus on safety and measurable KPIs.

Join a team advancing foundation models and AI solutions across Bosch products, collaborating with researchers and engineers to push the boundaries of trustworthy AI in real-world applications.

Qualifications

• Bachelor's 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.

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.

Skills

Python

LLMs

Agentic systems

ML fundamentals

Experiment design

Communication

Education

Bachelor's or Master's in CS or Engineering

Tools

RAG systems

Agent frameworks

Job description

Company Description

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 Vision and Language AI Group advances cutting‑edge research in two core AI areas: large language models (LLMs) and 3D computer vision. In the LLM space, we focus on AI agents, retrieval‑augmented generation, and effective adaptation of LLMs to Bosch domain‑specific applications. In 3D vision, we advance spatial intelligence with 3D world models that perceive, reconstruct, and simulate the physical world, enabling reliable embodied control and sim‑to‑real transfer. By integrating these two areas, we aim to build intelligent systems that can perceive, reason, and communicate seamlessly across both language and spatial environments. We also actively collaborate with leading groups in academia and industry to promote research ideas and publish research findings in internationally renowned conferences and journals such as ACL, EMNLP, CVPR, ICCV, ECCV, NeurIPS, AAAI and CoRL.

Job Description

• 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.

Basic Qualifications

• 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

Preferred Qualifications

• 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

Additional Information

We offer a competitive base salary for this position with a range in US-California of --$165,000 - $180,000 along with an annual corporate bonus, and a long-term incentive bonus designed to reward sustained impact and contribution over time. Within the salary range, the individual pay is determined based on several factors, including, but not limited to, work experience and job knowledge, complexity of the role, job location, etc.

• premium health coverage
• 401(k) with generous matching
• resources for financial planning and goal setting
• ample paid time off
• parental leave
• comprehensive life and disability protection

Your Recruiter can share more details for this position during the interview process.

Learn more about our full benefits offerings by visiting: https://www.myboschbenefits.com/public/welcome.

Equal Opportunity Employer, including disability / veterans.