NVIDIA

NVIDIA

Posted via Workday

Senior Deep Learning Software Engineer, DLSim

Posted Aug 25, 2026

Role at a glance

Salary
$152K – $287.5K/yr
Location
Santa Clara, California, United States
Work arrangement
On-site
Employment
Full-time
Experience
3+ years of relevant experience in compiler optimization, architectural simulation, or related areas.
Education
A Masters (or equivalent experience) in Computer Science, Computer Engineering, or a related STEM field; PhD preferred.

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Role Summary

AI-generated

The Deep Learning Performance Modeling Team develops full-stack simulation infrastructure for deep learning applications across GPUs. The Deep Learning Software Engineer will help build software that evaluates AI-accelerating GPU hardware and software advancements and informs future GPU, silicon, and system-level design decisions.

What You'll Do

  • Develop simulation backends for fast, scalable evaluation of AI workloads across NVIDIA compiler stacks
  • Improve deep learning compiler kernel code generation and computational graph optimization using modeled scenarios and performance insight
  • Advance the modeling and optimization of datacenter-scale AI workloads and deployment scenarios
  • Partner with architects and software teams to evaluate future GPU features
  • Guide silicon and system-level design decisions

Generated from the employer's posting. Verify important details before applying.

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Qualifications

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.

Required

  • Strong hands-on experience with MLIR and compiler infrastructure
  • Excellent C/C++ and Python programming skills
  • Software design, debugging, performance analysis, and test development

Preferred

  • Experience designing and building compiler frameworks or intermediate representations from the ground up
  • Deep understanding of LLM inference workloads and their implications for computer architecture
  • Hands-on experience implementing and optimizing complex AI workloads on CPUs, GPUs, or custom accelerators

Original job description

Content provided by the employer

The DL Performance Modeling Team’s core mission is to deliver full stack simulation infrastructure for deep learning applications across a spectrum of GPUs. We actively collaborate with architecture, software, product, and research teams to shape and refine the strategic roadmap of DL hardware and software

We are now looking for a Deep Learning Software Engineer to help develop simulation infrastructure. The software rapidly assesses new AI-accelerating GPU hardware and software advancements.

What you’ll be doing: 

  • Develop simulation backends that enable fast, scalable evaluation of AI workloads across NVIDIA compiler stacks.

  • Improve deep learning compiler kernel code generation and computational graph optimization using analysis based on modeled scenarios and performance insight.

  • Advance the modeling and optimization of datacenter-scale AI workloads and deployment scenarios.

  • Partner with architects and software teams to evaluate future GPU features and guide silicon and system-level design decisions.

What we need to see: 

  • A Masters (or equivalent experience) 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, with the ability to thrive in a fast-paced, multi-functional, outcome-focused environment.

Ways to stand out from the crowd:

  • Experience designing and building compiler frameworks or intermediate representations from the ground up.

  • Deep understanding of LLM inference workloads and their implications for computer architecture.

  • Hands-on experience implementing and optimizing complex AI workloads on CPUs, GPUs, or custom accelerators.

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, collaborative and love a challenge, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until August 28, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA

About the company

NVIDIA

Large Enterprise

NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.