Senior Deep Learning Software Engineer, DLSim
Company websitenvidia.com (NVIDIA, opens in new tab)
Base salary $152,000 to $287,500
Employer-published annual base salary, USD. Advertised base range in Santa Clara, CA +2 more
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationSanta Clara, CA +2 more
- WorkplaceWorkplace not stated
- Status at last checkSource accepting applications when checked
- Last checked (2 h ago) · observed since
All places named
- Santa Clara, CA
- Austin, TX
- Hillsboro, OR
About this job
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 other objective job-related factors. The expected base salary ranges for this position are: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 November 3, 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.Source: NVIDIA on Workday · View original posting (opens in new tab)
Pay in context
This role's base floor ($152,000) is above 21% of the 1,510 Data & ML roles in California listed on this site; 20 others list the same floor. Their median floor is $187,500; the middle half runs from $159,256 to $229,800.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 7, 2026.
This listingSenior Deep Learning Software Engineer, DLSim$152,000 to $287,500
Senior Networking Architect$152,000 to $287,500
Senior Product Quality Engineer, Board – Automotive$148,000 to $287,500
Solutions Architect, AI and ML$152,000 to $287,500
Infrastructure Solutions Architect - OEM$152,000 to $287,500
Senior Solutions Architect, First Time Deployment Networking Spectrum-X - NVIS$148,000 to $235,750
Senior Software Engineer, CUTLASS Performance$152,000 to $287,500
Chart as a table
| Role | Published base |
|---|---|
| Senior Deep Learning Software Engineer, DLSim (this listing) | $152,000 - $287,500 |
| Senior Networking Architect | $152,000 - $287,500 |
| Senior Product Quality Engineer, Board – Automotive | $148,000 - $287,500 |
| Solutions Architect, AI and ML | $152,000 - $287,500 |
| Infrastructure Solutions Architect - OEM | $152,000 - $287,500 |
| Senior Solutions Architect, First Time Deployment Networking Spectrum-X - NVIS | $148,000 - $235,750 |
| Senior Software Engineer, CUTLASS Performance | $152,000 - $287,500 |
Observed changes
- First observed on the employer's board
- Began meeting the published-salary criteria
- Observed since
- Employer-stated posting date
- 2026-10-30
Where this pay comes from
Location as the employer wrote it: US, CA, Santa Clara • US, TX, Austin • US, OR, Hillsboro (Levels 3-4 combined)
152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.
- Method
- Employer text, read with fixed rules and re-verified
- Checked
- Rule set
- us-base-v2
The pay passage appears in the employer's own job text. Fixed, deterministic rules located it, and the passage below was checked against the stored copy of the posting.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.