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Gpu Performance Engineer Jobs in Arizona (NOW HIRING)

Prosum Inc.

Sr. Machine Learning Engineer

Phoenix, AZ · On-site

$130K - $150K/yr

Sr. Machine Learning Engineer Salary Range: $130k to $150k Our client is seeking a Sr. Machine ... Profile and optimize system performance across CPU, GPU, memory, and I/O subsystems. GPU ...

CNH Industrial N.V.

Senior Software Engineer

Tempe, AZ · On-site

$117K - $154K/yr

You will build high-performance, scalable, and maintainable software components that serve as the ... Collaborate with AI, vision, and embedded teams to integrate and optimize GPU-accelerated ...

Intel

Principal Engineer - Standard Cell Design

Phoenix, AZ · On-site

  • Medical

  • Retirement

  • PTO

Come build what's next in scalable, high-performance silicon. At Intel, our standard cell libraries ... Your work will directly influence how our designs scale across CPU, GPU, and emerging architectures.

Intel

Sr. Machine Learning Engineer

Phoenix, AZ · On-site

$103K - $142K/yr

  • Medical

  • Retirement

  • PTO

Debug and optimize training runs - Profile training jobs, resolve bottlenecks, improve GPU ... How post-training techniques actually move model performance * How to make small models punch above ...

Introl

Data Center Technician

Phoenix, AZ · On-site

$30 - $36/hr

  • Medical

  • Dental

  • Vision

  • Retirement

Our network of 1,000+ field engineers operates globally, tackling the most complex deployments in ... performance and business demand. Key Responsibilities GPU Infrastructure & Hardware Management ...

Introl

Data Center Technician

Phoenix, AZ · On-site

$30 - $36/hr

  • Medical

  • Dental

  • Vision

  • Retirement

Our network of 1,000+ field engineers operates globally, tackling the most complex deployments in ... performance and business demand. Key Responsibilities GPU Infrastructure & Hardware Management ...

Accenture

AI & HPC Infrastructure Engineer

Scottsdale, AZ

$108K - $142K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... performance, scalability, resiliency, and governance needs * Deploy, configure, and manage XPU-based clusters (GPU, DPU, LPU, CPU) across bare-metal and containerized environments using workload ...

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Gpu Performance Engineer information

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 are popular job titles related to Gpu Performance Engineer jobs in Arizona?

For Gpu Performance Engineer jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Gpu Performance Engineer jobs in Arizona look for?

The top searched job categories for Gpu Performance Engineer jobs in Arizona are:

What cities in Arizona are hiring for Gpu Performance Engineer jobs?

Cities in Arizona with the most Gpu Performance Engineer job openings:

Gpu Performance Engineer jobs near you

Sr. Machine Learning Engineer

Prosum Inc.

Phoenix, AZ • On-site

$130K - $150K/yr

Other

Posted 11 days ago

Job description

Job Description
Sr. Machine Learning Engineer
Salary Range: $130k to $150k
Our client is seeking a Sr. Machine Learning Engineering for a direct hire role to sit in North Phoenix, AZ or Hillsboro, OR. This role will be onsite 4 days a week and 1 remote day.
JOB SUMMARY
The role of Senior Machine Learning Engineer will architect and optimize real-time, high-throughput, and ultra-low latency image pipelines for next-generation Mask Inspection Tools. Responsibilities include eliminating hardware bottlenecks through CUDA kernel tuning and GPU parallel computing, ensuring deep learning models and CV algorithms seamlessly processing massive, high-bandwidth streaming data at production scale.
ESSENTIAL DUTIES AND RESPONSIBILITIES
High-Performance Computing Pipeline Architecture
  • Design, implement, and optimize high-throughput, low-latency image processing pipelines for real-time optical inspection and machine vision systems.
  • Develop scalable architectures capable of processing large volumes of imaging data while meeting stringent latency and reliability requirements.
  • Profile and optimize system performance across CPU, GPU, memory, and I/O subsystems.
GPU Acceleration
  • Design, develop, and optimize CUDA kernels to accelerate deep learning inference and classical computer vision algorithms.
  • Maximize GPU utilization through efficient memory management, kernel optimization, and parallel programming techniques.
  • Evaluate and implement performance improvements using NVIDIA GPU technologies and profiling tools.
Model Deployment & Optimization
  • Optimize, quantize, and deploy machine learning models using TensorRT, ONNX Runtime, or similar inference frameworks.
  • Integrate AI models into production-grade C++ and Python applications.
  • Improve inference throughput, latency, and resource utilization while maintaining model accuracy.
  • Develop automated deployment and validation pipelines for machine learning models.
Concurrency & Systems Optimization
  • Architect and implement multi-threaded, high-concurrency software components for data acquisition, buffering, streaming, and real-time processing.
  • Design robust synchronization and communication mechanisms between hardware interfaces and AI processing pipelines.
  • Optimize end-to-end system performance for deterministic, real-time execution.
Cross-Functional Collaboration
  • Partner with machine learning scientists, computer vision engineers, hardware engineers, and software developers to deliver integrated AI solutions.

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