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# Deep Learning Compiler Engineer

- **Company:** NVIDIA Ltd.
- **Location:** Austin, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $152,000.0 - $241,500.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Neural Networks, C++ (Programming Language), Nvidia CUDA, Computer Engineering, Software Debugging, Python (Programming Language), OpenCL, Performance Tuning, Software Engineering, Graphics Processing Unit (GPU), Pytorch, Deep Learning, LLVM, Information Technology, Optimization Algorithms, MLIR (Multi-Level Intermediate Representation)
- **Published:** October 11, 2026
- **Apply:** https://startup.jobs/senior-deep-learning-compiler-engineer-2100-nvidia-usa-10368623

## About the Role

* Bachelors, Masters or Ph.D. in Computer Science, Computer Engineering, related field or equivalent experience
 * 3+ years of relevant work or research experience in performance analysis and compiler optimizations.
 * Ability to work independently, define project goals and scope, and lead your own development efforts.
 * Excellent C/C++ and Python programming and software design skills, including debugging, performance analysis, and test design.
 * Strong interpersonal skills are required along with the ability to work in a dynamic product-oriented team.

Ways to stand out from the crowd:

 * Proficient in CPU and/or GPU architecture. CUDA or OpenCL programming experience.
 * Experiences in systems with constrained resources, such as embedded platforms, small memory size, and cross compilation.
 * Experience with the following technologies: MLIR, XLA, TVM, LLVM, deep learning models and algorithms, and deep learning frameworks, such as PyTorch.
 * GPU kernel generation with high performance and fast build time.
 * A track record of success in mentoring junior engineers and interns is a bonus.

## Description

* Analyzing deep learning networks and developing compiler optimization algorithms.
 * Collaborating with members of the deep learning software framework teams and the hardware architecture teams to accelerate the next generation of deep learning software.
 * Scope of these efforts includes defining public APIs, performance optimizations and analysis, crafting and implementing compiler infrastructure techniques for neural networks, and other general software engineering work.