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Gpu Performance Engineer Jobs in Banning, CA (NOW HIRING)

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Gpu Performance Engineer information

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How much do gpu performance engineer jobs pay per hour?

As of Aug 28, 2026, the average hourly pay for gpu performance engineer in Banning, CA is $62.50, according to ZipRecruiter salary data. Most workers in this role earn between $51.25 and $70.72 per hour, depending on experience, location, and employer.

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 some common challenges faced by a GPU performance engineer 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 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.

What job categories do people searching Gpu Performance Engineer jobs in Banning, CA look for?

The top searched job categories for Gpu Performance Engineer jobs in Banning, CA are:

What cities near Banning, CA are hiring for Gpu Performance Engineer jobs?

Cities near Banning, CA with the most Gpu Performance Engineer job openings:

Gpu Performance Engineer jobs near you
Infographic showing various Gpu Performance Engineer job openings in Banning, CA as of August 2026, with employment types broken down into 83% Full Time, 14% Part Time, 1% Temporary, and 2% Contract. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution, with an average salary of $130,000 per year, or $62.5 per hour.

[Remote in US] AI Kernel Engineer - RISC-V Software Stack

San Bernardino, CA • On-site

Mentium Technologies Inc.
See more jobs at Mentium Technologies Inc.external

Other

Medical, Dental, Vision, Retirement, PTO

Posted 4 days ago

Job description

Overview

Mentium Technologies Inc. is seeking an Embedded Software Engineer to develop and optimize high-performance compute software for our custom RISC-V-based vision AI accelerator.

You will work at the intersection of embedded systems, computer architecture, and machine learning, developing high-performance compute kernels, runtime components, libraries, and developer-facing SDK tools. A key part of the role will be efficiently mapping compute-intensive workloads such as convolution, matrix multiplication, and signal-processing operations onto a multicore RISC-V SoC.

The role focuses heavily on vector/SIMD execution, memory optimization, data movement, multicore parallelism, and low-level performance optimization.

Prior RISC-V experience is valuable but not required. Engineers with backgrounds in ARM NEON/SVE, x86 SIMD/AVX, DSP software, GPU kernel programming, embedded performance optimization, or other low-level parallel architectures are encouraged to apply.

You will collaborate closely with RTL design, system architecture, software, and machine learning teams to turn architectural capabilities into a practical, high-performance, and extensible software platform.


Key Responsibilities

  • Develop and optimize high-performance ML and DSP compute kernels, including operations such as convolution, matrix multiplication, activation functions, pooling, image-processing primitives, and related numerical workloads
  • Optimize computationally intensive C/C++ code for vector/SIMD execution, multicore processing, and the SoC memory hierarchy
  • Build reusable compute libraries, runtime components, APIs, and developer-facing components for the Mentium SDK
  • Develop efficient data-movement, memory-management, and workload-scheduling strategies
  • Optimize the use of caches, scratchpad memories, DMA engines, and on-chip memory resources
  • Profile workloads and identify compute, memory-bandwidth, synchronization, and system-level performance bottlenecks
  • Perform low-level performance analysis using profiling, benchmarking, cycle measurements, and hardware/software debugging tools
  • Integrate optimized compute kernels and runtime components into AI model deployment and inference workflows
  • Develop functional tests, performance benchmarks, reference examples, and SDK documentation
  • Collaborate closely with RTL and system-architecture engineers to validate hardware features and improve end-to-end system performance
  • Contribute to the architecture and programming model of Mentium's RISC-V accelerator software stack
  • Evaluate and adapt relevant open-source libraries, runtimes, compiler technologies, and numerical software
  • Help define software requirements and provide feedback that influences future hardware architecture


Required Qualifications

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field, or equivalent practical experience
  • 3+ years of combined relevant industry, graduate research, doctoral research, or applied research experience
  • Strong programming skills in C and/or C++
  • Experience developing or optimizing performance-critical software
  • Experience with at least one area of low-level performance programming, such as:
  • SIMD or vector programming
  • DSP programming
  • GPU kernel programming
  • Assembly or intrinsic-based optimization
  • Performance-critical embedded software
  • Numerical or high-performance computing
  • Solid understanding of computer architecture, memory systems, and parallel processing
  • Experience with performance profiling, benchmarking, low-level debugging, or cycle-level optimization
  • Familiarity with computational workloads such as convolution, matrix multiplication, image processing, signal processing, or other numerical kernels
  • Ability to reason about memory access patterns, data locality, computational efficiency, and hardware utilization
  • Ability to read hardware specifications and work effectively with hardware and RTL engineers
  • Proficiency with Python for testing, automation, benchmarking, tooling, or application development
  • Experience with Git and standard collaborative software-development practices
  • Strong written and verbal communication skills


Preferred Qualifications

Experience in several of the following areas is valuable, but we do not expect candidates to have experience with all of them:

  • RISC-V instruction-set architecture or the RISC-V Vector Extension (RVV)
  • ARM NEON or SVE, x86 SSE/AVX, DSP vector architectures, GPUs, or other SIMD/vector processors
  • Vector intrinsics, assembly programming, compiler intrinsics, or low-level code optimization
  • DSP, image-processing, numerical-computing, or machine-learning kernel development
  • Quantized inference, fixed-point arithmetic, INT8/INT16 computation, FP16/BF16, or other reduced-precision numerical formats
  • DMA, scratchpad memory, cache hierarchies, memory bandwidth optimization, and multicore synchronization
  • Embedded, bare-metal, real-time, or resource-constrained software development
  • Multicore SoCs or heterogeneous compute architectures
  • Open-source RISC-V platforms such as PULP or similar multicore/accelerator systems
  • Machine-learning frameworks and model formats such as PyTorch, TensorFlow, TFLite, or ONNX
  • Compiler and deployment technologies such as LLVM, MLIR, TVM, Deeploy, or related systems
  • SDKs, runtime libraries, numerical libraries, developer tools, or reusable software APIs
  • Hardware-software co-design, SoC development, FPGA prototyping, architectural simulation, or custom accelerator development
  • Open-source software or research software development


Why Join Mentium?

At Mentium, you will work at the intersection of custom silicon, RISC-V, high-performance embedded software, and AI.

You will work directly with the engineers designing the underlying hardware and play a central role in determining how developers and machine-learning workloads interact with our accelerator.

Rather than simply programming an existing processor, you will have the opportunity to influence the hardware-software boundary: identifying architectural bottlenecks, developing optimized compute kernels, evaluating new programming approaches, and providing feedback that can shape future generations of the hardware.

Benefits:

  • Competitive compensation packages
  • Opportunity to work on diverse, cutting-edge AI projects across a range of industries.
  • 401(k)
  • Flexible PTO
  • Full PPO medical, dental, and vision insurance coverage


Apply