Senior Solutions Architect, AI Performance Engineering

Nvidia

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $184,000–$287,500 / yr
Posted: 56 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $206k

This role $236k

$157k most similar roles pay here $301k

This role pays more than 74% of similar roles. Most pay $171,125–$241,750 — the shaded band above. At the midpoint, this role pays about $236k versus about $206k for comparable roles.

Based on 239 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 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Senior Solutions Architect, AI Performance Engineering

Role Posting Log in to save

As a Senior Solutions Architect, AI Performance Engineering on the Automotive Solutions Architecture team, you will help autonomous vehicles and robotics customers accelerate Physical AI workloads using full-stack technologies. You will engage with application engineers to identify problems, develop fundamental parallel algorithms and data structures, and provide efficient GPU solutions through library development and direct application contributions. Your work involves deep optimization of high-performance operators including GPU kernel, instruction-level, and compiler optimizations. You will support libraries like cuDNN, cuBLAS, and CUTLASS while improving distributed transformer workloads using NCCL, NVSHMEM, and InfiniBand/RoCE protocols. Required skills include C, C++, or Python, Linux proficiency, and strong mathematical fundamentals in linear algebra. You will leverage expertise in parallel programming, high-performance computing, and CUDA kernels to solve complex performance bottlenecks for large-scale model training and inference.

What does a Solutions Architect earn in California?

Median $235750 from 45 postings across 8 companies.

See salary data

Skills

CUDA GPU C++ Python Linux cuDNN cuBLAS CUTLASS NCCL NVSHMEM InfiniBand RoCE NVLINK Nsight Compute High-Performance Computing Parallel Programming

What you'll do

Engage with customer engineers to identify and solve performance bottlenecks in autonomous vehicle workloads.
Develop and improve fundamental parallel algorithms and data structures for Physical AI applications.
Provide efficient GPU solutions through library development and direct contributions to customer applications.
Perform deep optimizations of high-performance operators, including GPU kernel tuning and compiler optimization.
Optimize distributed transformer workloads using NVIDIA communication tools like NCCL and NVSHMEM.
Develop efficient data transfer strategies by analyzing interconnect topologies and network protocols.
Influence the development of next-generation hardware, software platforms, and programming models based on workload insights.

What we're looking for

Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, Physics, Mathematics, or a related technical field.
8+ years of hands-on validated ML/DL performance engineering experience focusing on GPU compute efficiency for training and inference workloads.
Proficiency in C, C++, or Python and experience with Linux systems.
Strong mathematical fundamentals including linear algebra and numerical methods.
Background in parallel programming and high-performance computing with knowledge of parallel architectures and performance tuning.
Experience in distributed communication optimization involving RDMA, GPU interconnects, and collective communication algorithms.
Ability to communicate technical ideas clearly through blog posts, GitHub, and presentations.
Desired experience includes writing CUDA kernels, using Nsight tools, and optimizing LLM or HPC systems.

Save