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 DoingCollaborating 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.
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.
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.
Skills Required
- BS, MS, PhD or equivalent experience in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related field
- 8+ years of experience in accelerated computing technologies at cluster scale (ideally including NVIDIA platforms)
- Strong programming skills in C, C++, or Python
- Practical experience identifying and resolving bottlenecks in large-scale training workloads or parallel applications
- Hands-on experience profiling and debugging large parallel applications
- Solid understanding of CPU and GPU architectures, CUDA, parallel filesystems, and high-speed interconnects
- Experience working with large compute clusters and their scheduling/resource management (e.g., SLURM or cloud clusters)
- Proficient knowledge of training pipelines and frameworks, including internal operations and performance attributes
- Experience debugging training pipelines running on thousands of GPUs in production environments
- Hands-on performance profiling and optimization using tools like Nsight Systems and Nsight Compute; knowledge of NCCL, MPI and low-level communication libraries
- Ability to debug stability issues across the application, frameworks, runtime libraries, schedulers, and hardware stack
- Understanding of LLM frameworks (PyTorch, Megatron-LM, NeMo) or inference tools (vLLM, Dynamo, TensorRT-LLM, Red Hat Inference Server, SGLang)
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
