1 week ago

Senior Solutions Architect, HPC and AI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 17 hours agoStatus: Live

Reposted: first listed 6 days ago

Job Summary

Deploy, debug, and optimize AI training and inference workloads on large-scale GPU clusters, partnering with internal framework developers and external customers across Europe. Benchmark new framework capabilities, analyze performance, and share actionable insights. Help customers resolve cluster stability and bottleneck issues, scale reliably on NVIDIA GPUs, and contribute to Europe’s Sovereign AI initiative by improving resiliency features in training pipelines.

Location: Germany, Switzerland, France

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Collaborate with training framework developers and product teams to stay ahead of new features and help partners adopt them effectively.
•Support deployment and debugging of AI workloads, improving efficiency on NVIDIA platforms.
•Benchmark framework features, analyze performance, and share actionable insights with customers and internal teams.
•Work directly with customers to solve cluster performance/stability issues, identify bottlenecks, and implement solutions.
•Guide customers in scaling workloads efficiently and reliably on latest-generation NVIDIA GPUs, including contributing to resiliency features in AI training pipelines.

Key Requirements

•BS, MS, PhD (or equivalent) in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or a related engineering field (or equivalent practical experience).
•8+ years of experience in accelerated computing at cluster scale, ideally with NVIDIA platforms.
•Strong programming skills in C, C++, or Python.
•Hands-on experience profiling and debugging large parallel applications and resolving bottlenecks in large-scale training workloads or parallel applications.
•Solid understanding of CPU/GPU architectures, CUDA, parallel filesystems, high-speed interconnects, and cluster scheduling/resource management (e.g., SLURM or cloud clusters).

Experience:8+ yearsAccelerated computingHPCAIGPU clustersTraining/inference

Education:

Skills:Problem-solvingPerformance optimizationCollaborationDebuggingTroubleshooting

Tech Stack:CUDACC++PythonNsight SystemsNsight ComputeNCCLMPISLURMPyTorchMegatron-LMNeMoVLLMDynamoTensorRT-LLMRedHat Inference ServerSGLangParallel filesystemsHigh-speed interconnectsLLM frameworks

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

1 week ago

Senior Solutions Architect, HPC and AI

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 17 hours agoStatus: Live

Reposted: first listed 6 days ago

Job Summary

Location: Germany, Switzerland, France

Workplace: Onsite

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Collaborate with training framework developers and product teams to stay ahead of new features and help partners adopt them effectively.
•Support deployment and debugging of AI workloads, improving efficiency on NVIDIA platforms.
•Benchmark framework features, analyze performance, and share actionable insights with customers and internal teams.
•Work directly with customers to solve cluster performance/stability issues, identify bottlenecks, and implement solutions.
•Guide customers in scaling workloads efficiently and reliably on latest-generation NVIDIA GPUs, including contributing to resiliency features in AI training pipelines.

Key Requirements

•BS, MS, PhD (or equivalent) in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or a related engineering field (or equivalent practical experience).
•8+ years of experience in accelerated computing at cluster scale, ideally with NVIDIA platforms.
•Strong programming skills in C, C++, or Python.
•Hands-on experience profiling and debugging large parallel applications and resolving bottlenecks in large-scale training workloads or parallel applications.
•Solid understanding of CPU/GPU architectures, CUDA, parallel filesystems, high-speed interconnects, and cluster scheduling/resource management (e.g., SLURM or cloud clusters).

Experience:8+ yearsAccelerated computingHPCAIGPU clustersTraining/inference

Education:

Skills:Problem-solvingPerformance optimizationCollaborationDebuggingTroubleshooting

Tech Stack:CUDACC++PythonNsight SystemsNsight ComputeNCCLMPISLURMPyTorchMegatron-LMNeMoVLLMDynamoTensorRT-LLMRedHat Inference ServerSGLangParallel filesystemsHigh-speed interconnectsLLM frameworks

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor