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# Deep Learning Software Engineer

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $241,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Code Generation, Computer Engineering, Data Centers, Software Debugging, Microprocessors, Python (Programming Language), Software Engineering, Systems Architecture, Graphics Processing Unit (GPU), Deep Learning, Information Technology, Model Inference, MLIR (Multi-Level Intermediate Representation)
- **Published:** October 11, 2026
- **Apply:** https://www.juju.com/job/1_ScrapingHub33470-JR2024154

## About the Role

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.

## Description

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.