Senior Deep Learning Software Engineer, DLSim

Posted Yesterday
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3 Locations
In-Office
152K-288K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop simulation infrastructure for evaluating deep learning workloads across NVIDIA GPUs and compiler stacks. Optimize compiler kernel code generation, computational graphs, and datacenter-scale AI deployments using performance modeling. Collaborate with architecture, software, product, and research teams to assess future GPU features and influence silicon and system design. The role requires strong MLIR, C/C++, and Python expertise, along with experience in compiler optimization, architectural simulation, or related areas.
Summary Generated by Built In

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.

Skills Required

  • Master’s degree or equivalent experience in Computer Science, Computer Engineering, or a related STEM field
  • 3+ years of relevant experience in compiler optimization, architectural simulation, or related areas
  • Strong hands-on experience with MLIR and compiler infrastructure
  • Strong C/C++ programming skills
  • Strong Python programming skills
  • Experience with software design, debugging, performance analysis, and test development
  • Strong communication and collaboration skills
  • PhD in Computer Science, Computer Engineering, or a related STEM field
  • 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
  • Experience implementing and optimizing complex AI workloads on CPUs, GPUs, or custom accelerators

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
  • Healthcare Strength Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
  • Retirement Support Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.

NVIDIA Insights

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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