NVIDIA

3 months ago

Senior HPC and AI Network Software Architect

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 15 hours agoStatus: Live

Job Summary

Lead the architecture of scalable distributed AI training and inference systems, working with libraries like NCCL/UCX/UCC, and collaborating with AI framework teams to optimize end-to-end performance across GPU, DPU, and interconnects. Build production-grade, low-latency software for frontier AI workloads, shaping platform evolution through hardware-software co-design and runtime innovations.

Location: Zurich

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Sr. Manager level

Key Responsibilities

•Build and evolve the architecture of scalable software systems for distributed AI training and inference, focusing on throughput, latency, resiliency, and memory efficiency across cluster-scale deployments.
•Develop and evaluate next-generation communication and runtime capabilities in libraries such as NCCL, UCX, and UCC, tailored to the evolving demands of frontier AI workloads.
•Partner with AI framework teams (e.g., TensorFlow, PyTorch, JAX) and internal platform teams to build integrations, explore new approaches, and improve end-to-end performance and reliability.
•Collaborate on hardware and system-level features across GPUs, DPUs, and interconnects to speed up data movement and enable new capabilities for training, inference, and model serving at scale.
•Drive innovation across runtime systems, communication libraries, and AI-specific protocol layers, helping turn new ideas into practical capabilities and robust implementations.

Key Requirements

•Ph.D., or equivalent industry experience, in computer science, computer engineering, or a closely related field.
•5+ years of experience in systems programming, parallel or distributed computing, high-performance networking, or large-scale data movement, including experience designing and building complex systems.
•Strong programming background in C++, Python, and ideally CUDA or other GPU programming models.
•Extensive hands-on experience with AI frameworks (e.g., PyTorch, TensorFlow, JAX).
•Demonstrated ability to develop high-throughput, low-latency systems and reason across software stacks, hardware capabilities, and bottlenecks.

Experience:5+ yearsHPCDistributed computingHigh-performance networkingAI

Education:PhD / Doctorate

Skills:CollaborationCommunicationProblem-solvingTeamwork

Tech Stack:C++PythonCUDATensorFlowPyTorchJAXNCCLUCXUCCGPURDMA

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

3 months ago

Senior HPC and AI Network Software Architect

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 15 hours agoStatus: Live

Job Summary

Location: Zurich

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Sr. Manager level

Key Responsibilities

•Build and evolve the architecture of scalable software systems for distributed AI training and inference, focusing on throughput, latency, resiliency, and memory efficiency across cluster-scale deployments.
•Develop and evaluate next-generation communication and runtime capabilities in libraries such as NCCL, UCX, and UCC, tailored to the evolving demands of frontier AI workloads.
•Partner with AI framework teams (e.g., TensorFlow, PyTorch, JAX) and internal platform teams to build integrations, explore new approaches, and improve end-to-end performance and reliability.
•Collaborate on hardware and system-level features across GPUs, DPUs, and interconnects to speed up data movement and enable new capabilities for training, inference, and model serving at scale.
•Drive innovation across runtime systems, communication libraries, and AI-specific protocol layers, helping turn new ideas into practical capabilities and robust implementations.

Key Requirements

•Ph.D., or equivalent industry experience, in computer science, computer engineering, or a closely related field.
•5+ years of experience in systems programming, parallel or distributed computing, high-performance networking, or large-scale data movement, including experience designing and building complex systems.
•Strong programming background in C++, Python, and ideally CUDA or other GPU programming models.
•Extensive hands-on experience with AI frameworks (e.g., PyTorch, TensorFlow, JAX).
•Demonstrated ability to develop high-throughput, low-latency systems and reason across software stacks, hardware capabilities, and bottlenecks.

Experience:5+ yearsHPCDistributed computingHigh-performance networkingAI

Education:PhD / Doctorate

Skills:CollaborationCommunicationProblem-solvingTeamwork

Tech Stack:C++PythonCUDATensorFlowPyTorchJAXNCCLUCXUCCGPURDMA

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