Senior Deep Learning Performance Architect

Nvidia

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: CA, Canada
Salary: $152,000–$241,500 / yr
Posted: 31 days ago
Freshness: Confirmed live yesterday
Nearby: 18 roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $226k

This role $197k

$136k most similar roles pay here $304k

This role pays less than 75% of similar roles. Most pay $197,687–$254,750 — the shaded band above. At the midpoint, this role pays about $197k versus about $226k 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.

Most-posted roles

View all roles at Nvidia

At a glance

TL;DR · Senior Deep Learning Performance Architect

Role Posting Log in to save

As a Senior Deep Learning Performance Architect - LPU, you will join a team focused on hardware-software co-design to push the boundaries of AI inference performance. You will design novel GPU and system architectures, construct and test popular deep learning algorithms, and analyze how hardware and software relationships influence future applications. Your daily work involves building efficient power and performance models for the AI inference stack to guide next-generation hardware architecture while collaborating with research and product teams. To succeed, you must possess a strong mathematical foundation in machine learning and expertise in C, C++, Python, CUDA, MPI, and OpenMP. You will solve complex problems regarding AI efficiency Pareto curves and model LLM performance. The role requires deep knowledge of computer architecture to optimize every cycle for advanced inference workloads and system-level performance modeling.

Skills

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

What you'll do

Design novel GPU and system architectures to improve 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 algorithm development.
Build efficient power and performance models of the AI inference stack.
Use performance modeling data to guide next-generation hardware architecture decisions.
Develop CUDA kernels or compilers for custom ASIC hardware to improve inference workloads.
Perform system-level performance profiling and analysis to document and publish results.

What we're looking for

MS or PhD in a relevant field such as CS, EE, or Math, or equivalent experience.
5+ years of relevant experience in the field.
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.
Experience in systems-level performance modeling, profiling, and analysis.
Background in improving AI Inference workloads by developing CUDA kernels or compilers for custom ASIC hardware.

Save