Senior Accelerated Computing Architect

Nvidia

Confirmed live yesterday Trusted

Quick summary

Work type
On-site
Location
Santa Clara, CA
Salary
$184,000–$287,500 / yr
Posted
127 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $206k
This role $236k
$153k most similar roles pay here $302k

This role pays more than 80% of similar roles. Most pay $177,250–$235,750 — the shaded band above. At the midpoint, this role pays about $236k versus about $206k 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 896 open roles on FindRole.

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

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At a glance

TL;DR · Senior Accelerated Computing Architect

As a Senior Accelerated Computing Architect, you will join the team developing software and system architectures for high performance computing, scientific computing, machine learning, AI, datacenter, and automotive computing. You will perform in-depth analysis and optimization to ensure peak performance on current and next-generation GPUs while creating core parallel algorithms, data structures, and reference codes. Your daily work involves analyzing the interplay between hardware and software architectures, collaborating with hardware design and research teams for software-hardware co-design, and publishing technical white papers or patents. The role requires expertise in C, C++, and Python, along with experience in CUDA or OpenCL programming models. You will also utilize MPI, OpenSHMEM/NVSHMEM, and Unix-style IPC APIs to solve complex problems involving multi-node communication and benchmarking workloads on GPU and CPU clusters using strong mathematical fundamentals like linear algebra.

What you'll do

  • Perform in-depth analysis and optimization to maximize performance on current and next-generation GPUs.
  • Create and optimize core parallel algorithms, data structures, and reference codes for GPU solutions.
  • Analyze the interplay between hardware and software architectures on programming models and applications.
  • Facilitate software-hardware co-design by analyzing accelerated computing applications.
  • Write white papers, conference publications, blog posts, and patent applications to document work.
  • Benchmark, profile, and characterize workloads across GPU and CPU clusters.

What we're looking for

  • MS or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
  • At least 6 years of relevant work experience.
  • Strong mathematical fundamentals including linear algebra and numerical methods.
  • Hands-on experience with massively parallel GPU programming models such as CUDA or OpenCL.
  • Strong knowledge of C and C++ including software design, programming techniques, and algorithms.
  • Experience benchmarking, profiling, and characterizing workloads on GPU and CPU clusters.
  • Familiarity with multi-node communication APIs like MPI or OpenSHMEM/NVSHMEM is a plus.
  • Familiarity with threading APIs for multicore CPUs, Unix-style IPC APIs, and Python is a plus.

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