Senior Solutions Architect, HPC and AI

NVIDIA Corporation; Berlin; Vor Ort; EUR 120.000 - 170.000; Vollzeit

Benefits dieser Stelle

Zusammenfassung

NVIDIA seeks a Senior Solutions Architect to deploy, debug, and optimize training and inference workloads on large GPU clusters across Europe. You will collaborate with framework developers and customers to adopt new features, diagnose bottlenecks, and improve efficiency of AI workloads on NVIDIA platforms.

You will troubleshoot cluster performance, scale workloads reliably on latest GPUs, and contribute to Sovereign AI initiatives in Europe.

Qualifikationen

• BS/MS/PhD or equivalent experience in CS/EE/Physics/Math or related field.
• 8+ years in accelerated computing technologies at cluster scale, preferably with NVIDIA platforms.
• Strong programming skills in C/C++ or Python.
• Experience identifying and resolving bottlenecks in large-scale training workloads.
• Hands-on profiling and debugging of large parallel applications.
• Solid understanding of CPU/GPU architectures, CUDA, rapid interconnects.

Aufgaben

• Collaborate with training framework developers to adopt new features.
• Assist with deployment, debugging, and efficiency of AI workloads on NVIDIA platforms.
• Benchmark framework features, analyze performance, share insights with customers and teams.
• Work with external customers to resolve cluster performance and stability issues.
• Guide customers in scaling workloads efficiently on NVIDIA GPUs.
• Contribute to Europe’s Sovereign AI initiative with resilient training pipelines.

Kenntnisse

C/C++

Python

CUDA

Profiling

Debugging

SLURM

NCCL/MPI

Ausbildung

BS/MS/PhD in CS/EE/Physics/Math

Tools

Nsight Systems

Nsight Compute

MPI/NCCL

Jobbeschreibung

We are seeking a Senior Solutions Architect with strong hands‑on experience in deploying, debugging, and optimizing training and inference workloads on large‑scale GPU clusters. As we support customers and partners across Europe in training models on ground breaking GPU infrastructure, we are looking for someone who enjoys solving complex challenges at the intersection of High Performance Computing and AI. Similarly, inference is increasing in its complexity with explosion of MOE models and disaggregated execution making inference truly a HPC workload. You don’t need to have expertise in every skill we mention, but we are especially interested in candidates who bring deep knowledge in at least few key areas to enable large scale AI workloads. If you can demonstrate hands‑on experience, we would love to hear from you.

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, PhD 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 PyTorch, Megatron‑LM, or NeMo, and how they affect compute layers like CPUs, GPUs, network and storage or understanding of inference tools such as vLLM, Dynamo, TensorRT‑LLM, RedHat Inference Server or SGLang.

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