Solutions Architect, Networking

Remote from: Canada
Salary, yearly, CAD: 135,000 - 220,000
Department: Software Engineering
Employment type: Full Time,
Job posted: 11 May 2026
Apply before: 11 Jun 2026
Experience level: Senior
Views / Applies: 348 / 55

About company

About NVIDIA

NVIDIA is a leader in AI computing and graphics technology.

Computer Hardware
1993

Actively Hiring

AI Summary

NVIDIA is seeking a Solutions Architect, Networking to design and deploy large-scale AI Factories across Canada. The role involves collaborating with customers to build end-to-end infrastructure, focusing on high-performance networking for generative AI and large language models. Candidates need 5+ years of experience in solution architecture, with expertise in GPU and networking technologies. The position offers a competitive salary and benefits package, with base salary ranging from 135,000 to 220,000 CAD. Travel up to 20% is required.

Job Complexity

Easy Hard

AI Insight The role requires deep technical expertise in networking, GPUs, and AI infrastructure, plus strong communication and leadership skills, making it challenging but not the hardest.

Salary Analysis

AI Insight The offered salary range of 135,000-220,000 CAD (approx. 100,000-163,000 USD) is competitive for a Solutions Architect role in Canada, aligning with market rates for senior networking and AI infrastructure positions.

Key Skills

Networking GPU AI Infrastructure Solution Architecture CUDA NCCL Linux Performance Tuning Data Center Generative AI

Cover Letter Sample

Dear Hiring Manager,

I am excited to apply for the Solutions Architect, Networking position at NVIDIA. With over 5 years of experience in solution architecture and a deep background in high-performance networking and GPU technologies, I am confident in my ability to design and deploy large-scale AI factories. My hands-on experience with NVIDIA platforms, including CUDA, NCCL, and NVSwitch, aligns perfectly with the requirements of this role.

I have a proven track record of collaborating with customers to build and optimize AI infrastructure, solving complex technical challenges, and delivering clear executive communications. I am particularly drawn to NVIDIA's mission to advance AI and would be thrilled to contribute to Canada's AI infrastructure landscape.

Thank you for considering my application. I look forward to the opportunity to discuss how my skills and experience can benefit your team.

Sincerely,
[Your Name]

Possible Interview Questions

Describe your experience with designing and deploying large-scale GPU clusters for AI workloads. What were the key challenges and how did you overcome them?

I led the deployment of a 1,000-GPU cluster for training large language models. Key challenges included network congestion and GPU communication bottlenecks. I optimized the network topology using NVIDIA's NVSwitch and implemented RDMA over Converged Ethernet to reduce latency. I also worked with the engineering team to tune NCCL parameters, resulting in a 30% improvement in training throughput.

How do you approach troubleshooting performance issues in a multi-node GPU environment?

I start by gathering metrics from tools like DCGM, UFM, and NVIDIA's profiling tools to identify bottlenecks at the node, network, or application level. I analyze GPU utilization, memory bandwidth, and network throughput. For example, if I see low GPU utilization, I check for CPU or network stalls. I then isolate the issue by running targeted benchmarks and collaborate with engineering to resolve driver or firmware issues.

Can you explain the role of high-performance networking technologies like RDMA in distributed AI training?

RDMA (Remote Direct Memory Access) allows direct memory access between nodes without CPU involvement, reducing latency and overhead. In distributed AI training, it enables fast gradient synchronization using collective operations like all-reduce. This is critical for scaling training across many GPUs, as it minimizes communication time and improves overall efficiency.

Describe a time you had to communicate a complex technical issue to non-technical stakeholders. How did you ensure they understood?

I once had to explain a network bottleneck to executives. I created a simple diagram showing data flow and used analogies like 'traffic jams on a highway.' I focused on the business impact: delayed project timelines and increased costs. I then proposed a solution with clear ROI. The executives approved the upgrade, and the project was completed on time.

How do you stay current with emerging technologies in AI and networking?

I regularly attend conferences like GTC and read NVIDIA's technical blogs. I also participate in online communities and contribute to open-source projects. For example, I recently experimented with the latest NCCL version and tested its performance on a test cluster. I also take online courses on new networking protocols like NVLink 4.0.

NVIDIA is seeking outstanding Networking Solutions Architects (SA) to help design and deploy large-scale AI Factories across Canada. In this role, you will collaborate with customers to build end-to-end infrastructure. You will become a trusted technical advisor working on exciting projects, focused on how high-performance networking enables generative AI, large language models, and production AI inference pipelines. You will also collaborate with a diverse set of internal engineering, product, and business teams on performance analysis and modeling of these large GPU clusters. You should be comfortable working in a dynamic environment and have hands-on experience with NVIDIA networking and GPU technologies. This is an excellent opportunity to be at the center of Canada’s rapidly growing AI infrastructure landscape.

What You Will Be Doing:

Becoming the trusted technical advisor for NVIDIA Cloud Partners in Canada to rapidly bring NVIDIA Data Center GPU and networking platforms to market at scale.
Collaborating directly with customers to build, deploy, and optimize large-scale AI training and inference infrastructure using NVIDIA technology.
Analyzing deployment and performance data, identifying product health trends, system bottlenecks, and operational risks.
Solve challenging technical problems involving GPUs, networking, drivers, containers, firmware, and distributed system interactions.
Deliver streamlined executive‑level communication on status, risks, progress, and required decisions.
Some travel to customer sites is required, up to 20%.

What We Need To See:

BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience)
5+ years of Solution Architecture (or similar Sales Engineering, Systems Engineering, Cloud Engineering, Solution Engineering).
Understanding of high‑performance networking technologies (e.g., RDMA, congestion control, high‑bandwidth interconnects), and their role in distributed AI workloads.
Hands‑on experience with bring‑up and validation of large‑scale NVIDIA GPU platforms, including multi‑GPU and multi‑node architectures.
Familiarity with NVIDIA system software stacks: CUDA, NCCL, NVSwitch/NVLink, driver behavior, and performance tuning.
Ability to identify performance bottlenecks at the cluster, node, accelerator, network, or application layer.
Strong Linux fundamentals across drivers, kernel subsystems, cgroups, containers, and node‑level performance analysis.
Excellent presentation, communication, and collaboration skills.

Ways To Stand Out From The Crowd:

Prior experience deploying or optimizing deep learning training and inference at scale in production environments on large GPU clusters.
Familiarity with NVIDIA hardware (such as GPUs, networking, storage) and systems technology such as NCCL, DCGM, UFM, Mission Control, Base Command Manager.
Demonstrated leadership resolving multi‑team infrastructure challenges across engineering, product, and customer groups.
A consistent record of taking GPU or infrastructure products from pilot to high‑volume deployment in large data center environments.

We make extensive use of conferencing tools, but occasional travel is required for on-site visit to customers and industry events. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative and autonomous, we want to hear from you!

Widely considered to be one of the world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 135,000 CAD – 185,000 CAD for Level 3, and 170,000 CAD – 220,000 CAD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May 15, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

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