below marketmost similar roles pay hereabove market
This role pays more than
51%
of similar roles. Most pay
$162,000–$235,750
— the blue band above.
At the midpoint, this role pays about
$197k
versus about
$199k
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.
As a Solutions Architect, Inference Deployments, you will lead architecture discussions and co-design scalable inference systems with product and engineering teams. You will define technical directions, integration milestones, and deployment plans across edge and cloud environments. Your daily responsibilities include deploying, testing, and profiling speech and action models for robotics and automation while optimizing latency, throughput, and memory use. You will manage synthetic data workflows, production inference deployments, and performance profiling. Required skills include expertise in AI/ML and GPU inference, Linux systems, containers, and inference runtimes or ML frameworks. You will utilize tools like TensorRT, Triton Inference Server, vLLM, and SGLang. The role focuses on solving complex production challenges for robotics and automation by balancing model optimization methods like quantization with hardware constraints.
What does a Solutions Architect earn in California?
Median $235750 from 57 postings across 11 companies.
Lead architecture discussions with customers to co-design scalable inference systems for edge and cloud environments.
Deploy, test, and profile speech and action models for robotics and automation on edge devices.
Optimize model latency, throughput, and memory usage within specific hardware constraints.
Lead the optimization of synthetic data workflows including generation, augmentation, and filtering.
Manage production inference deployments including configuration, monitoring, capacity planning, and performance testing.
Evaluate and implement model optimization methods such as quantization, kernel selection, and batching.
Create deployment recipes, demos, and onboarding materials to drive customer adoption of NVIDIA technologies.
Resolve complex technical issues across models, runtimes, and infrastructure.
What we're looking for
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
5+ years of professional experience in software or hardware engineering, developer relations, technical partnerships, solutions architecture, or product management.
3+ years of direct, hands-on experience in AI inference or ML systems.
Deep expertise in AI/ML and GPU inference with a track record of driving product integrations and delivering production systems.
Hands-on experience building AI agents and working with Linux systems, containers, and at least one inference runtime or ML framework.
Demonstrated expertise in performance profiling, production deployment, monitoring, and troubleshooting.
Ability to explain complex technical concepts clearly to both technical and non-technical audiences.
Experience with NVIDIA Jetson, TensorRT Edge-LLM, Nemotron Speech, NVIDIA Riva, ROS 2, or NVIDIA Isaac (preferred).