Senior Solutions Architect, AI – Accelerated Physics

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Logo of NVIDIA

NVIDIA

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Partner with research universities and institutes to co-create innovative HPC and AI solutions using NVIDIA’s accelerated computing platform • Collaborate with engineering, product, and business teams to align NVIDIA’s technical roadmap with the evolving strategies and complex workflows of the scientific and engineering research community • Engage with developers and researchers to architect ground-breaking solutions in areas such as computational physics, multiphysics simulation, engineering design exploration, and the next generation of Scientific Foundation Models • Help researchers move from high-fidelity simulation data to AI-enabled workflows, including surrogate models, neural operators, physics-informed models, reduced-order models, differentiable simulation, and real-time inference • Profile and optimize the performance of scientific applications, AI training, and inference workloads so sophisticated research workflows reach their full potential on accelerated systems • Travel requirement up to 20%

🎯 Requirements

• BS, MS or PhD in Computational Physics, Engineering, Computer Science, Applied Mathematics, or a related field, or equivalent experience • 8+ years of hands-on experience in accelerated computing and knowledge of parallel computing with GPUs • Experience porting and/or optimizing scientific or engineering applications targeting GPUs • Strong fundamentals in programming and software design, especially in Python and C++ • Familiarity with computational physics or engineering simulation workflows, including numerical methods, model validation, uncertainty/error analysis, or simulation-data pipelines • Excellent knowledge of the theory and practice of AI at scale, especially as applied to scientific, simulation, or physics-based workloads • A dedication to clear and inclusive communication with a deep desire to partner with the academic community to help others succeed in their research goals

🏖️ Benefits

• highly competitive salaries • a comprehensive benefits package

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