Senior Solutions Architect

Nvidia

Confirmed live today Low trust

Remote

Quick summary

Work type: Remote
Location: Santa Clara, CATXNYVAWACA
Salary: $184,000–$287,500 / yr
Employment: Full-time
Posted: 60 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $201k

This role $236k

$152k $302k

below market most similar roles pay here above market

This role pays more than 78% of similar roles. Most pay $166,800–$235,750 — the blue band above. At the midpoint, this role pays about $236k versus about $201k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 1623 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1110 roles with salary data.

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At a glance

TL;DR · Senior Solutions Architect

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JOB TITLE: Senior Solutions Architect, NVIDIA Cloud Partners As a Senior Solutions Architect within the NVIDIA Cloud Partners team, you will serve as a trusted technical advisor bridging the design to deployment of large-scale AI and HPC GPU infrastructure. You will collaborate with cloud partners and internal engineering teams to create, implement, and deliver innovative hardware and software solutions. Your daily responsibilities include conducting technical meetings, building proofs of concept, and preparing reference architectures to address critical business needs. You will utilize Python, PyTorch, and TensorFlow to fine-tune and deploy models while integrating software stacks like TensorRT-LLM and the NeMo Framework. This role solves complex technical challenges in distributed cloud architectures, focusing on training and inference workloads across hybrid environments using technologies like Kubernetes, SLURM, and CUDA-X.

What does a Solutions Architect earn in California?

Median $235750 from 57 postings across 11 companies.

See salary data

Skills

Python PyTorch TensorFlow Deep Learning LLMs NVIDIA CUDA-X TensorRT TensorRT-LLM NVIDIA NeMo NVIDIA Triton Inference Server MLOps SLURM AWS Azure GCP NCCL DCGM UFM Cluster Orchestration

What you'll do

Act as the primary technical driver for customers during the design, construction, and production of GPU Cloud infrastructure.
Integrate libraries, frameworks, models, and software applications to drive consumption of GPU platforms.
Deliver GenAI, AI, and ML hardware and software solutions to production for strategic customers and partners.
Conduct technical customer meetings for project details, feature discussions, technology introductions, and debugging sessions.
Build and adopt NVIDIA solutions, including Proof of Concepts (PoCs), to address critical customer business needs.
Prepare and deliver technical content such as presentations, workshops, reference architectures, and tutorials.
Own end-to-end technology solution integration and provide product strategy recommendations based on customer feedback.
Align with internal teams to assess technical needs and secure business opportunities for NVIDIA hardware and software.

What we're looking for

BS/MS/PhD in Electrical/Computer Engineering, Computer Science, Physics, Mathematics, or other Engineering fields or equivalent experience.
10+ years of Solution Engineering, Sales Engineering, Cloud Engineering, or Solution Architecture experience.
Experience crafting and deploying large-scale cluster environments and designing distributed Cloud architectures.
Strong fundamentals in programming, optimizations, and software design, especially in Python and Deep Learning frameworks like PyTorch and TensorFlow.
Practical expertise fine-tuning and deploying models and integrating software application stacks, libraries, and frameworks for GPU platforms.
Excellent presentation, communication, and collaboration skills to drive complex multi-disciplinary technical engagements.
Practical experience with NVIDIA GPUs, software libraries, frameworks, and foundation models (preferred).
Hands-on expertise with scaled AI cloud environments (AWS, Azure, GCP) and on-premises/hybrid infrastructure (preferred).

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