Senior Solutions Architect, HPC and AI

Solutions EngineerSolutions EngineerFull TimeRemoteSeniorTeam 10,001+Since 1993H1B SponsorCompany SiteLinkedIn

Location

United Kingdom

Posted

4 days ago

Salary

€77.3K - €133.9K / year

Seniority

Senior

Postgraduate Degree8 yrs expExperience acceptedEnglishCloudPython

Job Description

Senior Solutions Architect, HPC and AI

NVIDIA

• 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.

Job 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.

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