🧑‍💻 I currently lead NVIDIA’s Local AI Agents effort, managing teams across multiple verticals to bring powerful AI agents to RTX Spark, DGX Spark, and other small form factor devices. My current focus is on maximizing intelligence per gigabyte and end-to-end performance, to make smaller models and agent harnesses more efficient so local agents can reliably complete complex tasks within limited memory and compute budgets. This work brings capable localized AI to a broader range of devices, making advanced agent experiences practical on smaller, more accessible hardware, with wide-reaching impact on data privacy, security, and on-device token generation for humanoids and unmanned autonomous mobile robots.

🪐 As a core contributor to NVIDIA Cosmos, I build world foundation models that help robots, autonomous vehicles, and other embodied systems learn in simulation before they act in the real world. My work also included creating Cosmos Guardrails and building systems for AI Safety and Cloud Security across Edify and Cosmos. I am also a founding member of the Nvidia AI Red Team 🚩 for safe, unbiased, and trustworthy multi-modal AI and a representative of NVIDIA at MLCommons, contributing to global AI safety standards such as MLCommons AILuminate 1.0 and Jailbreak 1.0.

🚘 Previously, I worked with the Autonomous Vehicles business unit at Nvidia, focusing on hyper-scale simulation, replay, and validation of self-driving vehicles in the cloud. I was instrumental in optimizing performance for DRIVE simulation and augmented re-simulator, significantly enhancing data-center utilization and workflow efficiency. My work has enabled autonomous vehicles to become safer, by training them to drive billions of miles in photorealistic virtual worlds by performing hardware-in-the-loop simulation. My earlier roles involved leading impactful projects in robot simulation and industrial automation for the software simulation leader (Dassault Systèmes SE) and the world’s largest 3-wheeler automaker (Bajaj Auto) respectively.

🎓 I hold a Master’s in Computer Science from Texas A&M University and developed multiple patented technologies in robotics. I have also authored a seminal work on disaster management using autonomous quadcopter swarms, published in a book chapter by Elsevier.

🌎 As a domain expert in AI and unmanned systems, I’ve contributed to various articles and conferences including showcasing the world’s first map of music at ACM Multimedia. Outside of work, I currently review for NeurIPS on the AI Ethics panel and judge various industry competitions. Previously, I’ve shared my expertise as an instructor at robotics workshops, such as RobArch at ETH Zurich, Switzerland.

🧑‍💻 Jibin currently leads NVIDIA’s Local AI Agents effort, managing teams across multiple verticals to bring powerful AI agents to RTX Spark, DGX Spark, and other small form factor devices. His current focus is on maximizing intelligence per gigabyte and end-to-end performance, to make smaller models and agent harnesses more efficient so local agents can reliably complete complex tasks within limited memory and compute budgets. This work brings capable localized AI to a broader range of devices, making advanced agent experiences practical on smaller, more accessible hardware, with wide-reaching impact on data privacy, security, and on-device token generation for humanoids and unmanned autonomous mobile robots.

🪐 As a core contributor to NVIDIA Cosmos, he builds world foundation models that help robots, autonomous vehicles, and other embodied systems learn in simulation before they act in the real world. His work also included creating Cosmos Guardrails and building systems for AI Safety and Cloud Security across Edify and Cosmos. He is also a founding member of the Nvidia AI Red Team 🚩 for safe, unbiased, and trustworthy multi-modal AI and a representative of NVIDIA at MLCommons, contributing to global AI safety standards such as MLCommons AILuminate 1.0 and Jailbreak 1.0.

🚘 Previously, he worked with the Autonomous Vehicles business unit at Nvidia, focusing on hyper-scale simulation, replay, and validation of self-driving vehicles in the cloud. He was instrumental in optimizing performance for DRIVE simulation and augmented re-simulator, significantly enhancing data-center utilization and workflow efficiency. His work has enabled autonomous vehicles to become safer, by training them to drive billions of miles in photorealistic virtual worlds by performing hardware-in-the-loop simulation. His earlier roles involved leading impactful projects in robot simulation and industrial automation for the software simulation leader (Dassault Systèmes SE) and the world’s largest 3-wheeler automaker (Bajaj Auto) respectively.

🎓 He holds a Master’s in Computer Science from Texas A&M University and developed multiple patented technologies in robotics. He has also authored a seminal work on disaster management using autonomous quadcopter swarms, published in a book chapter by Elsevier.

🌎 As a domain expert in AI and unmanned systems, he has contributed to various articles and conferences including showcasing the world’s first map of music at ACM Multimedia. Outside of work, he currently reviews for NeurIPS on the AI Ethics panel and judges various industry competitions. Previously, he shared his expertise as an instructor at robotics workshops, such as RobArch at ETH Zurich, Switzerland.