Senior Solutions Architect, HPC and AI

What You’ll Be Doing

• 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.

What We Need To See

• BS, MS, Ph D or equivalent experience in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or a related engineering field—or equivalent practical experience.
• 8+ years of experience in accelerated computing technologies at cluster scale, ideally including work with NVIDIA platforms.
• 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.

Ways To Stand Out From The Crowd

• Experience in debugging training pipelines running on thousands of GPUs in production environment.
• Hands-on experience with performance profiling and optimizations using tools like Nsight Systems, Nsight Compute and good understanding of NCCL, MPI and low-level communication libraries.
• Ability to debug stability issues across the entire stack: parallel application, training frameworks, runtime libraries, schedulers, and hardware.
• Solid understanding of the internal workings of LLM frameworks such as Py Torch, Megatron-LM, or Ne Mo, and how they affect compute layers like CPUs, GPUs, network and storage or understanding of inference tools such as v LLM, Dynamo, Tensor RT-LLM, Red Hat Inference Server or SGLang.
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