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Gpu Performance Engineer Jobs in Phoenix, AZ (NOW HIRING)

Prosum Inc.

Sr. Machine Learning Engineer

Phoenix, AZ · On-site

$150K - $198K/yr

Our client is seeking a Sr. Machine Learning Engineering for a direct hire role to sit in North ... Profile and optimize system performance across CPU, GPU, memory, and I/O subsystems. GPU ...

New

Intel

Sr. Machine Learning Engineer

Phoenix, AZ · On-site

$103K - $142K/yr

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

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

Litchfield Park, AZ · On-site

$30 - $36/hr

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

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 ...

Belcan

Avionics Software Engineer

Phoenix, AZ · Hybrid

$100K - $185K/yr

... performance and avoid runtime stalls. · Perform software integration tests, unit tests, and other ... Preferred Qualifications & Skills: · Display integration experience (touchscreen, HUD, and/or GPU ...

onsemi

Applications Engineer

Scottsdale, AZ · On-site

... GPU/AI compute platforms and largescale powerdelivery clusters. You will work closely with ... We are committed to sourcing, attracting, and hiring high-performance innovators, while providing ...

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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 10, 2026, the average hourly pay for gpu performance engineer in Phoenix, AZ is $59.68, according to ZipRecruiter salary data. Most workers in this role earn between $48.94 and $67.55 per hour, depending on experience, location, and employer.

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 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.

What are popular job titles related to Gpu Performance Engineer jobs in Phoenix, AZ? For Gpu Performance Engineer jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Gpu Performance Engineer jobs in Phoenix, AZ look for? The top searched job categories for Gpu Performance Engineer jobs in Phoenix, AZ are:
What cities near Phoenix, AZ are hiring for Gpu Performance Engineer jobs? Cities near Phoenix, AZ with the most Gpu Performance Engineer job openings:
Gpu Performance Engineer jobs near you

Sr. Machine Learning Engineer

Prosum Inc.

Phoenix, AZ • On-site

$150K - $198K/yr

Other

Posted 3 days ago

New

Job description

Job Description
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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