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Gpu Performance Engineer Jobs in California (NOW HIRING)

Google

Software Engineer, GPU Performance

Sunnyvale, CA · On-site

$164K/yr

Experience low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques. * Experience with modern GPU architectures (NVIDIA, AMD, or other AI accelerators), memory ...

RadixArk

Performance Engineer

Palo Alto, CA · On-site

P99 latency, TTFT, tokens/sec/GPU, throughput under long-context workloads, cost-per-million tokens ... Strong systems engineering background, especially in performance-critical software * Experience ...

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Gpu Performance Engineer information

What are some common challenges faced by GPU Performance Engineers when optimizing graphics workloads?

GPU Performance Engineers often encounter challenges such as identifying performance bottlenecks within complex graphics pipelines, balancing resource utilization, and achieving optimal frame rates across diverse hardware configurations. They must use specialized profiling tools and collaborate closely with developers, driver engineers, and QA teams to address issues like memory bandwidth limitations or shader inefficiencies. Staying updated with rapidly evolving GPU architectures and optimizing for both current and next-generation hardware are also key aspects of the role.

What is a GPU Performance Engineer?

A GPU Performance Engineer is a specialist who analyzes, optimizes, and improves the performance of graphics processing units (GPUs). They work on identifying bottlenecks, optimizing code, and ensuring that GPU hardware and software deliver maximum efficiency and speed. Their role may involve working with drivers, firmware, and applications to enhance graphics and compute workloads. This job is essential in industries like gaming, AI, and high-performance computing where GPU efficiency directly impacts user experience and system performance.

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

To thrive as a GPU Performance Engineer, you need a strong background in computer architecture, programming (C/C++), and a degree in computer science, electrical engineering, or a related field. Proficiency with GPU profiling tools (e.g., NVIDIA Nsight, AMD Radeon GPU Profiler), performance analysis frameworks, and parallel computing libraries like CUDA or OpenCL is typically required. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaborating with developers and debugging performance bottlenecks. These skills and qualities are essential for optimizing GPU performance, ensuring efficient software-hardware interaction, and delivering high-quality graphics or compute solutions.

What is the difference between Gpu Performance Engineer vs Gpu Hardware Engineer?

AspectGpu Performance EngineerGpu Hardware Engineer
Primary FocusOptimizing GPU performance, benchmarking, and tuning softwareDesigning, developing, and testing GPU hardware components
Required SkillsProgramming, performance analysis, GPU architecture knowledgeHardware design, circuit analysis, FPGA/ASIC experience
Work EnvironmentSoftware development teams, labs for testing performanceHardware labs, manufacturing facilities, R&D centers
Common CertificationsNone specific, often requires computer engineering or related degreesElectrical engineering, VLSI design certifications

The Gpu Performance Engineer primarily focuses on optimizing and testing GPU software performance, while the Gpu Hardware Engineer designs and develops the physical GPU components. Both roles require a strong background in computer engineering, but differ in their core responsibilities and work environments.

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Gpu Performance Engineer jobs near you
Data Center GPU Performance Engineer - Product

Data Center GPU Performance Engineer - Product

Nvidia

Santa Clara, CA

Full-time

Re-posted yesterday

Nvidia rating

9.3

Company rating: 9.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

15th of 209 rated software companies

Job description

NVIDIA has been redefining 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. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world!

What you'll be doing:

NVIDIA's Accelerated Computing team is a driving force behind the explosion of Machine Learning, Artificial Intelligence and High-Performance Computing. We are looking for a highly capable individual with a consistent track record in technology and the skills for GPU product definition for Data Center. We are a small, dynamic, and motivated team that defines the next generation of products for these high growth markets.

  • Guide the architecture of the next-generation of GPUs through an intuitive and comprehensive grasp of how GPU architecture affects performance for datacenter applications, especially Large Language Models (LLMs)

  • Drive the discovery of opportunities for innovation in GPU, system, and data-center architecture by analyzing the latest data center workload trends, Deep Learning (DL) research, analyst reports, competitive landscape, and token economics

  • Find opportunities where we uniquely can address customer needs, and translate these into compelling GPU value proposition and product proposals

  • Distill sophisticated analyses into clear recommendations for both technical and non-technical audiences

What we need to see:

  • 5+ years of total experience in technology with previous product management, AI related engineering, design or development experience highly valued

  • BS or MS or equivalent experience in engineering, computer science, or another technical field. MBA a plus.

  • Deep understanding of fundamentals of GPU architecture, Machine Learning, Deep Learning, and LLM architecture with ability to articulate relationship between application performance and GPU and data center architecture

  • Ability to develop intuitive models on the economics of data center workloads including data center total cost of operation and token revenues

  • Demonstrated ability to fully contribute to above areas within 3 months

  • Strong desire to learn, motivated to tackle complex problems and the ability to make sophisticated trade-offs

Ways to stand out from the crowd:

  • 2+ years direct experience in developing or deploying large scale GPU based AI applications, like LLMs, for training and inference

  • Ability to quickly develop intuitive, first-principles based models of Generative AI workload performance using GPU and system architecture (FLOPS, bandwidths, etc.)

  • Comfort and drive to constantly stay updated with the latest in deep learning research (academic papers) and industry news

  • Track record of managing multiple parallel efforts, collaborating with diverse teams, including performance engineers, hardware architects, and product managers

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 148,000 USD - 224,250 USD for Level 3, and 168,000 USD - 258,750 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until January 16, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse 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

Hours and flexibility

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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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