Senior Solutions Architect, HPC – AI

November 4

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NVIDIA

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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.

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

📋 Description

• Collaborating with NVIDIA’s training framework developers and product teams to stay ahead of the latest features and help partners to adopt them effectively. • Assisting with deployment, debugging, and improving the efficiency of AI workloads on extensive NVIDIA platforms. • Benchmarking new framework features, analyzing performance, and sharing actionable insights with both customers and internal teams. • Working directly with external customers to solve cluster performance and stability issues, identify bottlenecks, and implement effective solutions. • Build expertise and guide customers in scaling workloads efficiently and reliably on the latest generation of NVIDIA GPUs. • Contributing to Europe’s Sovereign AI initiative by helping customers implement advanced resiliency features within AI training pipelines.

🎯 Requirements

• 8+ years of experience in accelerated computing technologies at cluster scale • Strong programming skills in at least one of the following languages: C, C++, or Python • Practical experience identifying and resolving bottlenecks in large-scale training workloads or parallel applications • Hands-on experienced in profiling and debugging large parallel applications • Solid understanding of CPU and GPU architectures, CUDA, parallel filesystems, and high-speed interconnects • Experienced in working with large compute clusters with an understanding of their internal scheduling and resource management mechanisms (e.g. SLURM or Cloud based clusters) • Proficient knowledge of training pipelines and frameworks, encompassing their internal operations and performance attributes.

🏖️ Benefits

• Health insurance • Flexible work arrangements • Professional development