Senior Deep Learning Software Engineer

Nvidia

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Santa Clara, CAAustin, TXHillsboro, OR
Salary: $152,000–$241,500 / yr
Posted: 18 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $226k

This role $197k

$137k most similar roles pay here $293k

This role pays less than 70% of similar roles. Most pay $196,750–$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.

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At a glance

TL;DR · Senior Deep Learning Software Engineer

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As a Senior Deep Learning Software Engineer, DLSim, you will join the DL Performance Modeling Team to develop simulation infrastructure that rapidly assesses new AI-accelerating GPU hardware and software advancements. Your daily responsibilities include developing simulation backends for scalable evaluation of AI workloads across NVIDIA compiler stacks, improving deep learning compiler kernel code generation, and optimizing computational graphs based on modeled performance insights. You will also advance the modeling of datacenter-scale AI workloads while partnering with architects to guide silicon and system-level design decisions. To succeed in this role, you must possess expertise in MLIR and compiler infrastructure alongside strong C/C++ and Python programming skills. The work focuses on solving complex problems regarding LLM inference workloads and their implications for computer architecture within the domain of high-performance computing and hardware acceleration.

Skills

MLIR C++ Python Compiler Optimization NVIDIA GPU LLM Inference Computational Graph Optimization Kernel Code Generation Performance Analysis AI Workloads Software Design Debugging Test Development

What you'll do

Develop simulation backends to enable fast, scalable evaluation of AI workloads across NVIDIA compiler stacks.
Improve deep learning compiler kernel code generation and computational graph optimization based on performance insights.
Model and optimize datacenter-scale AI workloads and deployment scenarios.
Evaluate future GPU features to guide silicon and system-level design decisions.
Build and maintain simulation infrastructure to assess new AI-accelerating hardware and software advancements.
Develop tools for analyzing modeled scenarios to improve overall system performance.

What we're looking for

Master's degree in Computer Science, Computer Engineering, or a related STEM field (PhD preferred).
3+ years of relevant experience in compiler optimization, architectural simulation, or related areas.
Strong hands-on experience with MLIR and compiler infrastructure.
Excellent C/C++ and Python programming skills including software design, debugging, performance analysis, and test development.
Strong communication and collaboration skills to work in a fast-paced, multi-functional environment.
Experience designing and building compiler frameworks or intermediate representations from the ground up (preferred).
Deep understanding of LLM inference workloads and their implications for computer architecture (preferred).
Hands-on experience implementing and optimizing complex AI workloads on CPUs, GPUs, or custom accelerators (preferred).