AI Research Scientist - Safety

Bosch Group•Sunnyvale, CA

7h•$165,000 - $185,000•Onsite

About The Position

The Bosch Research and Technology Center North America (RTC-NA) is a part of the global Bosch Group. Our AI research in Silicon Valley focuses on Foundation Models, Big Data Visual Analytics, Explainable AI (XAI), Natural Language Processing, Computer Vision & Mixed Reality, Cloud Robotics, Data Science, AI System Engineering, and Time-series Analysis. We develop scalable, intelligent, and trustworthy AIoT solutions for Bosch products and services in application areas such as automated driving, advanced driver assistance systems (ADAS), robotics, smart manufacturing, enterprise AI, health care, smart home and building solutions. The Foundation Model Powered AI Enablers group plays a pivotal role in shaping the future of industrial AI experiences for Bosch products and services. By fusing cutting-edge machine learning, data analysis, and interactive visualization technologies, we research and develop scalable and transparent AI & big data analytic solutions for a range of domains, including Industry 4.0 (I4.0), IoT, autonomous driving, and connected vehicles. Our award-winning team actively collaborates with leading academic and industry groups to advance research ideas and disseminate findings in top AI conferences and journals.

Requirements

Ph.D. in Computer Science, Machine Learning, Robotics, Artificial Intelligence, or a related quantitative field.
3+ years of dedicated research experience (academic or industry) focusing on world models, generative dynamics, self-supervised learning, or physical reasoning for autonomous systems.
2+ years of industrial R&D experience in creating, fine-tuning, and deploying large-scale machine learning models and safety-critical algorithms into production or testing environments.
Hands-on engineering proficiency in Python and deep learning frameworks (PyTorch or JAX).

Nice To Haves

Deep theoretical and practical expertise in AI safety techniques, including alignment, adversarial robustness, interpretability (XAI), failure mode analysis, and uncertainty quantification.
Strong low-level engineering experience with C++ and high-performance distributed training pipelines for system integration.
Proven track record of peer-reviewed publications in top-tier machine learning or robotics venues (e.g., NeurIPS, CVPR, ICCV, ECCV, ICLR, ICRA).
Demonstrated experience generating intellectual property and securing patent contributions in trustworthy AI or autonomous systems.

Responsibilities

Lead strategic research and engineering initiatives in AI safety, alignment, and model reliability, with direct application to ADAS feature releases and autonomous systems.
Advance generative world models, simulation frameworks, and physical reasoning techniques to evaluate complex driving edge cases and validate system behavior.
Collaborate with global R&D teams to seamlessly transfer cutting-edge safety frameworks and machine learning algorithms into operational platforms and production pipelines.
Implement rigorous safety validation, out-of-distribution (OOD) detection, failure mode analysis, and uncertainty estimation methods to ensure robust platform integration.
Monitor emerging developments in foundation models and safety alignment, representing the organization by presenting at top-tier conferences (e.g., NeurIPS, CVPR, ICLR, ICRA) and publishing high-impact research.
Provide expert technical counsel to senior leadership to guide strategic R&D roadmaps, investment decisions, and patent portfolio growth in trustworthy AI.