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About the Role

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. Inference is increasing in its complexity with the 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.

Responsibilities

Collaborate with NVIDIA’s training framework developers and product teams to stay ahead of the latest features and help partners to adopt them effectively.
Assist with deployment, debugging, and improving the efficiency of AI workloads on extensive NVIDIA platforms.
Benchmark new framework features, analyze performance, and share actionable insights with both customers and internal teams.
Work 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.
Contribute to Europe’s Sovereign AI initiative by helping customers implement advanced resiliency features within AI training pipelines.

Requirements

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.

Skills

Debugging training pipelines running on thousands of GPUs in production environment
Performance profiling and optimizations using tools like Nsight Systems, Nsight Compute
NCCL
MPI
Low-level communication libraries
Debugging stability issues across the entire stack: parallel application, training frameworks, runtime libraries, schedulers, and hardware
Internal workings of LLM frameworks such as PyTorch, Megatron-LM, or NeMo
Compute layers like CPUs, GPUs, network and storage
Inference tools such as vLLM, Dynamo, TensorRT-LLM, RedHat Inference Server or SGLang

Experience Level

8+ years of experience

Education Level

BS, MS, PhD or equivalent experience in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or a related engineering field—or equivalent practical experience.