ZipRecruiter

Log In

1

Gpu Compiler Engineer Jobs in Renton, WA (NOW HIRING)

Meta

Software Engineer, LLVM Compiler

Bellevue, WA · On-site +1

$154K/yr

Software Engineer, LLVM Compiler Responsibilities: * Design and implement LLVM compiler passes ... Experience working closely with hardware architectures such as SIMD, GPU, RISC-V, and AI ...

New

OpenAI

Software Engineer, GPT Infrastructure

Seattle, WA · On-site

$293K - $385K/yr

Integrate model workloads, hardware profiles, compiler toolchains, runtimes, serving engines, and ... Familiarity with GPU or accelerator architecture, memory hierarchies, interconnects, collective ...

previous page

Showing results 41-60

All JobsGpu Compiler Engineer JobsGpu Compiler Engineer Jobs in Renton, WA

Gpu Compiler Engineer information

See Renton, WA salary details

$12.4K

$93.7K

$127.1K

How much do gpu compiler engineer jobs pay per year?

As of Sep 11, 2026, the average yearly pay for gpu compiler engineer in Renton, WA is $93,688.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,900.00 and $121,500.00 per year, depending on experience, location, and employer.

What is a GPU compiler engineer?

GPU Compiler Engineers are specialized software engineers who design, develop, and optimize compilers that translate high-level programming code into machine code that runs efficiently on Graphics Processing Units (GPUs). Their work enables developers to harness the full power of GPUs for tasks such as graphics rendering, scientific computing, and machine learning. These engineers often work closely with hardware teams to ensure compatibility and performance, and they play a critical role in advancing GPU technology.

What are some common challenges faced by GPU compiler engineers in optimizing code for different hardware architectures?

GPU Compiler Engineers often encounter challenges when adapting and optimizing code for a wide variety of GPU architectures. Each hardware platform may have unique instruction sets, memory hierarchies, and performance characteristics, requiring tailored compiler optimizations. Balancing performance, portability, and correctness is an ongoing challenge, especially when supporting multiple vendors or generations of hardware. Close collaboration with hardware engineers and performance analysts is essential to ensure the compiler produces efficient code that leverages the strengths of each GPU architecture.

What are the key skills and qualifications needed to thrive as a GPU compiler engineer, and why are they important?

To thrive as a GPU Compiler Engineer, you need a strong background in computer science, with expertise in compiler design, parallel programming, and GPU architectures, typically supported by a relevant degree. Familiarity with tools and languages such as LLVM, CUDA, OpenCL, and performance profiling systems is essential. Analytical thinking, problem-solving ability, and effective collaboration are crucial soft skills for success in this role. These skills ensure the development of high-performance, reliable compiler solutions that optimize GPU-based applications and support innovation in computing.

What is the difference between Gpu Compiler Engineer vs Gpu Software Engineer?

AspectGpu Compiler EngineerGpu Software Engineer
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related; knowledge of compiler designBachelor's or Master's in Computer Science or related; strong programming skills
Work EnvironmentResearch and development teams focused on compiler optimization and hardware integrationSoftware development teams working on GPU applications, drivers, or SDKs
Industry UsagePrimarily in hardware and compiler companies, GPU manufacturers

The Gpu Compiler Engineer specializes in developing and optimizing compilers for GPU hardware, focusing on translating high-level code into efficient machine instructions. In contrast, the Gpu Software Engineer works on creating GPU-related software, such as drivers, SDKs, or applications. While both roles require strong programming skills and knowledge of GPU architecture, the compiler engineer emphasizes compiler design and optimization, whereas the software engineer focuses on software development and integration.

What are popular job titles related to Gpu Compiler Engineer jobs in Renton, WA?

For Gpu Compiler Engineer jobs in Renton, WA, the most frequently searched job titles are:

What job categories do people searching Gpu Compiler Engineer jobs in Renton, WA look for?

The top searched job categories for Gpu Compiler Engineer jobs in Renton, WA are:

What cities near Renton, WA are hiring for Gpu Compiler Engineer jobs?

Cities near Renton, WA with the most Gpu Compiler Engineer job openings:

Gpu Compiler Engineer jobs near you
Infographic showing various Gpu Compiler Engineer job openings in Renton, WA as of August 2026, with employment types broken down into 92% Full Time, 2% Part Time, and 6% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $93,688 per year, or $45 per hour.

Senior Inference Engineer, GPU Kernel Optimization

Seattle, WA • On-site

Nvidia
Computer and Electronic Product Manufacturing • 10K+ employees
Learn more about Nvidiaexternal

$139K - $183K/yr

Full-time

Re-posted 15 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

Job description

We're now looking for a Sr. Inference Engineer, for GPU Kernel Optimization! What does it take to push every LLM inference operation to its performance ceiling? Our LLM Inference Performance Analysis and Optimization team builds the answer from the ground up. We develop silicon-measured kernel benchmarking infrastructure, model-level performance projection tooling, and agentic optimization systems that improve GPU kernels at the assembly layer. Our team works closely with compiler, kernel, hardware, and framework organizations across NVIDIA to surface bottlenecks and ship measurable gains. If driving GPU performance at the frontier of LLM inference sounds like your kind of challenge, we'd love to meet you!

What you'll be doing:

The role drives three interconnected systems, all aimed at accelerating NVIDIA's LLM inference stack. The first is GPU kernel microbenchmarking: measuring competing kernel implementations at real-silicon fidelity across the full configuration space that production LLM deployments demand. The second is end-to-end model performance analysis: connecting performance evidence to model-level serving economics, surfacing high-value optimization opportunities, and producing optimization policies for production inference deployments. The third is agentic kernel optimization: applying AI-driven analysis to diagnose performance gaps, explore optimization opportunities across the kernel ecosystem, and validate findings with rigorous silicon measurements. All three streams converge in close collaboration with compiler, hardware, kernel, and framework teams to deliver upstream improvements and production-grade performance gains.

What we need to see

  • Master's or PhD in Computer Science, Computer Engineering, or a related field, or equivalent experience.

  • 6+ years of relevant industry experience.

  • Experience building or directing agentic AI systems - code generation, automated optimization, or multi-step reasoning workflows.

  • Strong Python and C++ skills with proven software engineering fundamentals.

  • Hands-on GPU profiling with CUPTI, NSYS, and NCU; proven track record to attribute bottlenecks across kernel execution, compiler decisions, and runtime scheduling.

  • Direct experience with LLM inference frameworks such as TRT-LLM, SGLang, or vLLM and clear understanding of how kernel selection drives model-level throughput and latency.

  • Working knowledge of GPU kernel optimization - CUDA, CUTLASS, Triton, or equivalent - and the ability to read PTX or SASS output.

Ways to stand out from the crowd

  • Deep knowledge of SASS/PTX-level kernel analysis, compiler middle-end optimization, or GPU code generation pipelines (LLVM, MLIR, ptxas, or similar).

  • Track record shipping agentic systems end-to-end - tool invent, multi-agent orchestration, and silicon-verified validation - within a performance engineering or kernel optimization context.

  • Active contributions to open-source LLM inference or GPU kernel libraries (FlashInfer, Triton, CUTLASS, or similar).

Widely considered to be one of the technology world's most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 31, 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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

View All Nvidia Jobs

Apply