Senior Solutions Architect, AI - Accelerated Physics

At NVIDIA, we believe that accelerated computing is key to solving the world’s most significant scientific and engineering challenges. We are looking for a Solutions Architect to join our Higher Education and Research Team, where you will serve as a technical partner to the visionaries shaping the future of discovery! In this role, you will be an integral part of a team supporting higher education universities and research institutes across the nation, with a focus on computational physics, engineering simulation, scientific AI, and high-performance computing. You will help researchers harness NVIDIA platforms to accelerate simulation, build trusted AI surrogates, train scientific foundation models, and unlock new workflows in areas such as fluid dynamics, multiphysics simulation, engineering design exploration, and physics-based modeling at scale. If reading this gets you excited and energized to help researchers solve hard computational physics and engineering problems, then we would love to meet you. What you'll be doing: * 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% What we need to see: * 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 Ways to stand out from the crowd: * Excellent GPU programming skills, including debugging, profiling, code optimization, performance analysis, and test design * Experience supporting HPC, AI, computational physics, engineering simulation, or scientific computing workflows. * Familiarity with NVIDIA scientific computing and AI tools such as PhysicsNeMo, NVIDIA Warp, PyTorch, JAX, or related frameworks * Experience building AI-enabled simulation workflows using neural operators, physics-informed models, graph neural networks, and/or reduced-order models. * A desire to learn and grow within an encouraging, forward-thinking community dedicated to solving the world’s most significant computational science and engineering challenges Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ [http://www.nvidiabenefits.com/] #NALASAHiring Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits [https://www.nvidia.com/en-us/benefits/]. Applications for this job will be accepted at least until July 20, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Partner with research universities to co-create innovative HPC and AI solutions using NVIDIA's accelerated computing platform. Architect ground-breaking workflows in computational physics and optimize AI training and inference workloads for scientific applications.

Requires a degree in Computational Physics, Engineering, Computer Science, or Applied Mathematics with 8+ years of experience in accelerated computing and GPU optimization. Must possess strong programming skills in Python and C++ and a deep understanding of AI at scale for physics-based workloads.

• Competitive Salaries • Comprehensive Benefits Package • Equity

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• Accelerated Computing
• GPU Programming
• Parallel Computing
• Python
• C++
• Computational Physics
• Engineering Simulation
• Scientific AI
• High-Performance Computing
• Neural Operators
• Physics-Informed Models
• Performance Profiling
• PyTorch
• JAX
• Numerical Methods
• Software Design

• Technology
• Science & Research
• Engineering
• Software
• Data & Analytics

Formation minimale

Baccalauréat

Expérience minimale

10+ ans

Langue de l’offre

anglais

Heures de travail

40 heures par semaine