Senior Deep Learning Performance Architect

Nvidia

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Hybrid

Quick summary

Work type: Hybrid
Location: CATX
Salary: $152,000–$241,500 / yr
Employment: Full-time
Posted: 77 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $223k

This role $197k

$137k $293k

below market most similar roles pay here above market

This role pays less than 70% of similar roles. Most pay $196,750–$250,000 — the blue band above. At the midpoint, this role pays about $197k versus about $223k 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 Deep Learning Performance Architect

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The Senior Deep Learning Performance Architect joins a team focused on hardware-software co-design to accelerate AI inference workloads. In this role, you will design novel GPU and system architectures to advance inference performance and efficiency. Daily responsibilities include constructing and testing deep learning algorithms, analyzing the relationship between hardware and software architectures, and building efficient power and performance models for the AI inference stack. You will collaborate across software, research, and product teams to guide future hardware decisions. Required skills include a strong mathematical foundation in machine learning, expert programming in C, C++, and Python, and familiarity with CUDA, MPI, and OpenMP. You must possess deep knowledge of computer architecture and experience in systems-level performance modeling, profiling, and characterizing AI inference workloads for custom ASIC hardware.

Skills

Deep Learning GPU Computing CUDA C++ Python C HPC MPI OpenMP Computer Architecture Performance Modeling Profiling AI Inference ASIC Compilers Machine Learning

What you'll do

Design novel GPU and system architectures to advance AI inference performance and efficiency.
Construct, investigate, and test popular deep learning algorithms and applications.
Analyze the relationship between hardware and software architectures to influence future algorithms.
Build efficient power and performance models of the AI inference stack.
Capture significant performance data to guide next-generation hardware architecture decisions.
Guide the direction of AI by working with software, research, and product teams.

What we're looking for

MS or PhD in a relevant field (CS, EE, Math) or equivalent experience.
5+ years of relevant experience.
Strong mathematical foundation in machine learning and deep learning.
Expert programming skills in C, C++, and/or Python.
Familiarity with GPU computing (CUDA or similar) and HPC (MPI, OpenMP) stack.
Strong knowledge and coursework in computer architecture.
Background with systems-level performance modeling, profiling, and analysis (preferred).
Experience in characterizing and modeling system-level performance, accomplishing comparison studies, and documenting and publishing results (preferred).