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Gpu Compiler Engineer Jobs in California (NOW HIRING)

General Motors

Staff ML Compiler Engineer

Sunnyvale, CA · On-site

You'lljoin a group ofdeepcompiler, systems, and GPU engineerswho enjoyworking onhard problems ... The Role As a Staff Compiler Engineer on the AI Kernels & Compilers team, you will own the endtoend ...

Nvidia

Senior Compiler Engineer - PVA

Santa Clara, CA · Hybrid

$122K - $168K/yr

NVIDIA is now looking for a Senior Compiler Engineer to join our Programmable Vision Accelerator ... Are you passionate about becoming a part of a best-in-class team supporting the latest in GPU and ...

Persimmons

Compiler Engineer (Frontend)

San Jose, CA · On-site

Provide technical leadership across the compiler team, mentoring engineers in advanced compiler ... and GPU-style parallelism. * Demonstrated fluency with modern AI tools and workflows (e.g ...

Persimmons

Compiler Engineer (Frontend)

San Jose, CA · On-site

Provide technical leadership across the compiler team, mentoring engineers in advanced compiler ... and GPU-style parallelism. * Demonstrated fluency with modern AI tools and workflows (e.g ...

Persimmons

Compiler Engineer (Frontend)

San Jose, CA · On-site

Provide technical leadership across the compiler team, mentoring engineers in advanced compiler ... and GPU-style parallelism. * Demonstrated fluency with modern AI tools and workflows (e.g ...

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Gpu Compiler Engineer information

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 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 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.
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Infographic showing various Gpu Compiler Engineer job openings in California 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.

Senior AI Compiler Engineer - Applied Research

Nvidia Corporation

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Re-posted 27 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies

Job description

NVIDIA's GPUs are at the core of modern AI infrastructure, from training large-scale models to running inference in production. That position depends on software as much as hardware, and compiler engineering is a big part of what makes it work.
We are looking for outstanding AI Research Engineer /Applied Scientist focused on Compilers /Low-level optimization to join the team and develop groundbreaking technologies in machine learning compilers and AI systems. We build innovative AI compiler solutions that work together with NVIDIA's software stack to provide comprehensive acceleration for modern machine learning models.
What you'll be doing:
  • Design and implement AI-based technology addressing core problems of low-level GPU code generation.
  • Build SFT and RL training pipelines.
  • Define model inputs using low-level compiler representations.
  • Define, implement, and evaluate strategies for intelligent prompt engineering in compilation domain.
  • Prototype and iterate on model architectures, prompts, and training strategies for NP-hard problems in optimizing compilers.
  • Prepare datasets from compiler traces, optimization passes, and target-specific performance signals.
  • Apply RL techniques to optimize for downstream objectives and run rigorous experiments, analysis, and benchmarking across workloads and hardware targets.
  • Build rigorous benchmarks to assess code quality, correctness, and generation overhead.
  • Partner with compiler engineers to integrate and ship learned policies with production toolchains.

What we need to see:
  • M.S. or PhD degree in Computer Engineering, Computer Science related technical field (or equivalent experience).
  • 5+ years of experience building AI/ML systems.
  • Solid understanding of machine learning fundamentals and experimentation best practices.
  • Strong software engineering skills in Python and C++.
  • Hands-on experience training/fine-tuning/post-training large models.
  • Experience with reinforcement learning.
  • Reward modeling from non-differentiable signals (binary runtime/compile success, performance counters).
  • Knowledge of prompt-engineering techniques (CoT, chaining/orchestration, context adaptation, etc).
  • Ability to work across research and engineering, from prototype to production.
  • CUDA programming experience and GPU performance familiarity.

Ways to stand out from the crowd:
  • Distributed training/inference at scale (Megatron, NeMo, vLLM, Triton).
  • Experience working with the NVIDIA training stacks.
  • Fundamentals of construction of optimizing compilers.
  • Understanding of GPU performance, experience with benchmarking suites and performance profiling tools.
  • Knowledge of formal methods or static analysis for correctness guarantees.

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous program manager with a real passion for technology, we want to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until July 28, 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

Year founded

1993

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