below marketmost similar roles pay hereabove market
This role pays more than
85%
of similar roles. Most pay
$159,400–$235,750
— the blue band above.
At the midpoint, this role pays about
$236k
versus about
$198k
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.
The Senior Solutions Architect, AI Performance Engineering joins the Automotive Solutions Architecture team to help Autonomous Vehicles and Robotics customers accelerate Physical AI workloads using NVIDIA full-stack technologies. This role involves engaging with application engineers to solve performance bottlenecks by developing fundamental parallel algorithms and data structures. You will provide efficient GPU solutions through library development, direct application contributions, and deep optimization of high-performance operators including GPU kernel, instruction-level, and compiler optimizations. Key technologies include C, C++, Python, Linux, cuDNN, cuBLAS, CUTLASS, DeepGEMM, FlashAttention, NCCL, NVSHMEM, and InfiniBand/RoCE. You will improve communication for distributed transformer workloads by studying interconnect topologies and developing data transfer strategies. The work focuses on improving GPU compute efficiency for training and inferencing workloads within the accelerated computing space.
What does a Solutions Architect earn in California?
Median $235750 from 57 postings across 11 companies.
Engage with AV application engineers to identify and solve performance bottlenecks in Physical AI workloads.
Develop and improve fundamental parallel algorithms and data structures for high-performance computing.
Provide efficient GPU solutions through library development and direct contributions to customer applications.
Perform deep optimization of high-performance operators, including GPU kernel, instruction-level, and compiler tuning.
Optimize distributed transformer workloads using communication tools like NCCL, NVSHMEM, and InfiniBand/RoCE protocols.
Develop efficient data transfer strategies to enable compute-communication overlap in large-scale model training.
Influence the development of next-generation hardware, software platforms, and programming models based on workload insights.
What we're looking for
BSc/MSc/PhD or equivalent experience 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 improving GPU compute efficiency for training and inferencing workloads.
Experience with C, C++, or Python and proficiency with Linux.
Solid understanding of software development, programming techniques, and algorithms.
Strong mathematical fundamentals, including linear algebra and numerical methods.
Background in parallel programming and high-performance computing with knowledge of parallel architectures and performance analysis.
Experience in GPU programming (preferred).
Experience in distributed communication optimization, including RDMA, GPU interconnects, and collective communication algorithms (highly helpful).
AI Systems
Computer Architecture
Performance Modeling
High-Performance Computing
Distributed Systems
Python
C++
CUDA
Triton
ROCm
HIP
Data Parallelism
HBM
PCIe
Compilers