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# Senior Deep Learning Software Engineer, DLSim

- **Company:** NVIDIA Ltd.
- **Location:** Monte Sereno, CA, United States
- **Experience:** Expert
- **Salary:** $152,000.0 - $241,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Code Generation, Computer Programming, Computer Engineering, 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 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-deep-learning-software-engineer-dlsim-austin-tx--80ae328d-34ae-4598-8b40-7a83b218f5ba

## About the Role

Software Engineering, Deep Learning, Software Engineering, Deep Learning, GPU (Graphics Processing Unit), Simulation, Artificial Intelligence (AI), Infrastructure Software, Kernel Programming, Analysis Skills, * 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

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, * 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.