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NVIDIA

Senior Solutions Architect, AI Inference

NVIDIA is hiring a Senior Solutions Architect, AI Inference in Santa Clara, United States. It pays $184k-$288k a year and Level rates it ; you can apply on NVIDIA.

Level's read on this job

  • Level rates this solutions architect role Builds AI ●●●●: the job is building AI systems.
  • The listed pay starts 9% above the typical solutions architect posting on Level.
  • Tools and skills the posting cites include vLLM.
  • Level classifies it as a senior role.
  • Level lists 2,700+ other open roles at NVIDIA.

AI in this role

vLLM

We are looking for an architect that wants to change how the AI inference industry works. With agent adoption taking off and GPUs in shortage, NVIDIA has a key part to play in solving the technical challenges that scale token generation on finite resources. Our ISP (inference service provider) team supports some of our most strategic partners who rely on SA support to push the boundaries of performance, squeezing value and reliability out of vast NCP (NVIDIA Cloud Partner) infrastructure.

You’ll master full-stack inference with every SOTA bell and whistle, from smarter routing to rack-scale disaggregation down to kernel optimization. You’ll write reference architectures adopted by 10k+ gpu clusters. You’ll teach C-level executives, distinguished engineers and datacenter developers how to scale their businesses, systems and infrastructure. This role covers a wide range of technical skills and the ability to lead high-stakes conversations with partners. Nobody can cover everything! For that reason, we're looking for someone with fast-learning technical agility and comfort with partners or customers. We’ll build a leading tech stack internally, then bake all our best tricks into a reference architecture and guide the industry to follow in our footsteps.

What you'll be doing:

  • Work with inference partners who build cutting-edge inference services, teach them the value of our stack and bring back product insights that benefit the whole industry.

  • Build and operate inference recipes with tools like NVIDIA Dynamo, distributing tasks among GPU workers to improve efficiency.

  • Accelerate inference pipelines using TensorRT-LLM, vLLM, SGLang, and other backends to ensure seamless integration with disaggregated inference.

  • Evangelize devops best-practices for such as managing kubernetes clusters, configuring compute fabrics, and observability.

  • Provide mentorship and technical leadership to customers and internal teams, guiding them through the deployment of disaggregated inference systems and resolving complex issues.

What we need to see:

  • 6+ Years in Solutions Architecture (or Solution Engineering, Forward-Deployed Engineering, or similar) driving customer engagements to deploy distributed systems, including 2+ years with AI workloads on Kubernetes.

  • Experience with one of NVIDIA Dynamo, Triton Inference Server, or TensorRT-LLM for model optimization and serving.

  • Deep knowledge of modern inference best practices including disaggregated serving, multi-tier KV cache management, speculative decoding, quantization and custom inference kernels.

  • Hands-on full-stack agent design with modern sandboxing, memory and retrieval systems with evaluation, skill design and governance.

  • BS in CS/Engineering or equivalent experience.

Ways to stand out from the crowd:

  • Prior experience deploying NVIDIA inference technologies such as Dynamo, NIXL and Grove.

  • Experience with model customization techniques such as SFT, DPO, GRPO, RLVR and corresponding data curation techniques.

  • Contributions to open-source projects relevant to the above.

Widely considered to be one of the technology 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 other objective job-related factors. The expected base salary ranges for this position are:
184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

How we rate this

Senior Solutions Architect, AI Inference at NVIDIA rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

vLLM

Also common in solutions architect jobs at this level

Pay

This job starts at
$184k
Typical solutions architect job
$169k

This posting starts 9% above the typical solutions architect job. See solutions architect jobs

Questions you could be asked

  1. What's a project where you used vLLM hands-on?
  2. How would you decide a model or AI system is ready to ship?
  3. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: vLLM. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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