Senior Solutions Architect, HPC and AI

NVIDIA Gruppe; Berlin; Vor Ort; EUR 120.000 - 180.000; Vollzeit

Zusammenfassung

NVIDIA Berlin seeks a senior individual contributor to optimize and scale AI training workloads on NVIDIA GPUs. You will collaborate with framework developers and customers to deploy and debug at scale, benchmarking new features and extracting actionable performance insights.

Ideal candidates have 8+ years in accelerated computing, strong C/C++/Python skills, and hands-on profiling with CUDA and Nsight tools. You will influence stack design and support sovereign AI efforts across Europe.

Qualifikationen

• Degree or equivalent practical experience in a relevant field.
• 8+ years in accelerated computing at cluster scale, preferably with NVIDIA platforms.
• Strong programming in C, C++ or Python.
• Experience identifying and resolving bottlenecks in large-scale training workloads.
• Hands-on profiling and debugging of large parallel applications.

Aufgaben

• Collaborate with framework developers and product teams on features.
• Assist deployment, debugging, and efficiency of AI workloads on NVIDIA platforms.
• Benchmark features, analyze performance, share insights with customers and teams.
• Work with external customers to fix cluster performance and stability issues.
• Help customers scale workloads efficiently on NVIDIA GPUs.
• Support Europe’s Sovereign AI initiatives by adding resiliency in pipelines.

Kenntnisse

C

C++

Python

Performance profiling

GPU architectures

CUDA

Parallel computing

SLURM

NCCL/MPI

Ausbildung

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

Tools

Nsight Systems

Nsight Compute

NCCL

MPI

Jobbeschreibung

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.