Solutions Architect - Physical AI TPM

About the Role

NVIDIA is a leading company in AI computing, and its employees are passionate about AI, HPC, and GAMING. The Solutions Architect (SA) team focuses on introducing NVIDIA's cutting-edge technology into various industries. This role is pivotal in designing the architecture of AI computing platforms and analyzing AI and HPC applications to deliver significant value to customers. A key focus is on defining and solving complex computational challenges related to Large Language Model (LLM) inference and training acceleration, alongside optimizing network communication and data transfer. The Solutions Architect will specifically track and drive Physical AI engagements across autonomous driving, robotics, and industrial automation, serving as a crucial technical product management interface between local customer engagements and NVIDIA's global product organizations. This ensures transparent communication of engagement status, milestones, blockers, and next steps, and coordination of joint projects to meet customer requirements and market expectations.

Responsibilities

• Track and drive ongoing Physical AI engagements across autonomous driving, robotics, and industrial automation, working closely with customers’ technical teams, local field teams, and global product teams.
• Serve as the technical product management interface between local customer engagements and NVIDIA product organizations, ensuring clear visibility into engagement status, milestones, blockers, and next steps.
• Coordinate joint projects between local SA teams, customers, partners, and global product / engineering teams, ensuring requirements are well documented, and progress is tracked through delivery.
• Deeply understand customers’ Physical AI workloads and requirements — including simulation, synthetic data generation, robot learning, digital twins, and embodied AI model development — and map them to NVIDIA platforms such as Isaac Sim, Isaac Lab, Omniverse, Cosmos, DRIVE, and related software stacks.
• Track and analyze industry trends, customer adoption patterns, and competitor solutions in autonomous driving, robotics simulation, embodied AI, digital twins, and world foundation models, and provide insights on how NVIDIA Physical AI products should evolve to meet market expectations.

Requirements

• 5+ years of experience in the technology industry in roles such as technical product manager, technical program manager, solutions architect, systems engineer, robotics engineer, or ML engineer, with a master’s degree or above in computer science, robotics, electronic engineering, automation, or related fields.
• Experience managing technical product or customer engagement programs, including tracking milestones, coordinating cross-functional stakeholders, managing risks, driving issue resolution, and communicating status to leadership.
• Strong interest in Physical AI, autonomous driving, robotics, simulation, and accelerated computing, with the passion to quickly go deep into new products, tools, and frameworks.
• Familiarity with one or more Physical AI domains such as robot simulation, autonomous vehicle development, reinforcement learning, synthetic data generation, digital twins, sensor simulation, or embodied AI model training.
• Excellent communication skills: able to explain complex technical concepts clearly to audiences with varied technical backgrounds, and able to structure documents, dashboards, and presentations in a concise and convincing way.
• Proficiency in written and spoken English and Chinese for collaboration with global product teams and local customers.

Qualifications

• Hands-on experience with NVIDIA Physical AI platforms such as Isaac Sim, Isaac Lab, Omniverse, Cosmos, DRIVE, or related simulation, robotics, and AI development tools.
• Experience owning or contributing to a product roadmap, or technical program plan in an AI software, robotics, simulation, or autonomous systems domain.
• Background in autonomous driving, robotics, embodied AI, synthetic data generation, world models, or large-scale simulation platforms.
• Strong understanding of Physical AI ecosystem trends, including robotics foundation models, world foundation models, synthetic data pipelines, and competing industry platforms