Senior Performance Compiler Engineer

Nvidia

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: Redmond, WASanta Clara, CA
Salary: $184,000–$287,500 / yr
Posted: 126 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $204k

This role $236k

$137k most similar roles pay here $304k

This role pays more than 84% of similar roles. Most pay $172,000–$235,750 — the shaded band above. At the midpoint, this role pays about $236k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Nvidia

Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing

Nvidia currently has 896 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 876 roles with salary data.

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At a glance

TL;DR · Senior Performance Compiler Engineer

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As a Senior Performance Compiler Engineer - Triton, you will join the team working on the open-source Triton compiler project to improve AI performance on NVIDIA GPUs. You will investigate GPU hardware architecture and programming models while analyzing advanced algorithms like attention sinks and MoEs to identify optimization opportunities. Your daily work involves designing and implementing compiler technology using MLIR to optimize high-level kernel descriptions written in Triton's Python DSL, with a focus on generating efficient low-level GPU code. When necessary, you will use inline PTX to hand-tune critical paths. You must possess expertise in C++, CUDA or OpenCL parallel programming, and assembly-level programming. This role addresses the technical challenge of accelerating training and inference for large language models by optimizing kernels through iterative compiler and hardware-level optimizations.

Skills

Triton MLIR C++ CUDA PTX Python OpenCL OpenMP BLAS Assembly High-Performance Computing Parallel Programming Linear Algebra

What you'll do

Investigate and analyze the latest NVIDIA GPU hardware architectures and programming models.
Analyze advanced AI algorithms and numerics to identify new opportunities for performance optimization.
Design and implement compiler technology using MLIR to optimize high-level kernel descriptions in Triton's Python DSL.
Generate efficient, low-level GPU code from high-level descriptions.
Use inline PTX to hand-tune critical code paths and extract peak hardware performance.
Execute an iterative optimization process across both kernels and compiler components to achieve maximum efficiency.
Influence future products by collaborating with hardware architects and the CUDA compiler team.

What we're looking for

Bachelor, Master, or PhD degree in Computer Science, Computer Engineering, Applied Math, or a related field.
8+ years of relevant industry experience in software development.
Strong C++ programming and software design skills with an emphasis on performance analysis and debugging.
Experience in parallel programming using CUDA, OpenCL, or other models like OpenMP.
Solid understanding of computer architecture and hands-on experience with assembly-level programming.
Experience with machine learning compilers such as TVM or MLIR.
Background in numerics, linear algebra, and tuning BLAS or deep learning library kernels.
Track record of contributions to open-source projects in the AI/ML, compiler, or high-performance computing domains.

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