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Gpu Engineer Jobs in Arizona (NOW HIRING)

Accroid Inc

Advanced Packaging Engineer

Phoenix, AZ · On-site

Bachelor'''''s degree in Electrical Engineering, Mechanical Engineering, Materials Science, Physics ... Experience supporting AI Accelerators, GPU, CPU, or ASIC products. * Knowledge of SI/PI analysis ...

CNH Industrial N.V.

Senior Software Engineer

Tempe, AZ · On-site

$117K - $154K/yr

Collaborate with AI, vision, and embedded teams to integrate and optimize GPU-accelerated processing pipelines. * Conduct code reviews and promote engineering best practices across the team. * Lead ...

Intel

Sr. Machine Learning Engineer

Phoenix, AZ

$103K - $142K/yr

Debug and optimize training runs - Profile training jobs, resolve bottlenecks, improve GPU ... Research-engineering balance: Ability to produce production-quality implementations of novel ...

Centraprise

Openshift and Python Developer

Chandler, AZ · On-site

$49.50 - $68.25/hr

Job Title : Openshift and Python Developer Job Location : Chandler, AZ (ONSITE) Job Type ... Drive GPU infrastructure enablement on OpenShift for Al/ML and GenAl use cases. o Build and ...

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Gpu Engineer information

See Arizona salary details

$36.3K

$94.8K

$128.1K

How much do gpu engineer jobs pay per year?

As of Aug 2, 2026, the average yearly pay for gpu engineer in Arizona is $94,822.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,300.00 and $108,600.00 per year, depending on experience, location, and employer.

What engineers make $300,000 a year?

Senior GPU engineers, especially those with extensive experience, advanced skills in graphics architecture, and expertise in programming languages like C++ and CUDA, can earn $300,000 or more annually. High-level roles in large tech companies or specialized fields such as AI and machine learning often offer compensation at this level, particularly with bonuses and stock options included.

What engineers make $200,000 a year?

Senior GPU engineers, especially those with extensive experience, advanced skills in graphics architecture, and proficiency with tools like CUDA or Vulkan, can earn $200,000 or more annually. High-level roles in large tech companies or specialized fields such as AI or data centers often offer such compensation packages.

What are the key skills and qualifications needed to thrive in the Gpu Engineer position, and why are they important?

To thrive as a GPU Engineer, you need strong knowledge of computer architecture, proficiency in C/C++, and experience with parallel programming models such as CUDA or OpenCL, along with a degree in computer science, electrical engineering, or a related field. Familiarity with debugging tools, driver development, performance profiling utilities, and hardware simulation platforms is typically required. Excellent problem-solving abilities, attention to detail, and effective teamwork and communication skills help distinguish top candidates. These skills ensure that GPU Engineers can develop high-performance solutions, efficiently troubleshoot hardware and software issues, and collaborate successfully in multidisciplinary environments.

What does a GPU engineer do?

A GPU engineer designs, develops, and optimizes graphics processing units and related hardware or software. They work on improving graphics performance, parallel processing, and computational efficiency, often using programming languages like C++ and tools such as CUDA or OpenCL. Their work supports applications in gaming, scientific computing, and machine learning.

What does a GPU Engineer do?

A GPU Engineer designs, develops, and optimizes graphics processing units (GPUs) for applications like gaming, artificial intelligence, and high-performance computing. They work on hardware architecture, driver development, and parallel computing optimizations to maximize performance. GPU Engineers collaborate with software developers, hardware designers, and researchers to improve graphics rendering, machine learning acceleration, and computational efficiency.

What engineer makes $500,000 a year?

Highly experienced GPU engineers working in top technology companies or specialized roles in graphics hardware development can earn salaries approaching or exceeding $500,000 annually, often including bonuses and stock options. Such compensation typically requires advanced skills in hardware design, software optimization, and extensive industry experience.

What are some common challenges faced by GPU Engineers, and how are they addressed?

GPU Engineers often face challenges such as optimizing code for maximum parallel efficiency, debugging complex hardware-software interactions, and keeping pace with rapidly evolving GPU architectures. Addressing these issues typically requires a combination of deep architectural understanding, use of specialized profiling and debugging tools, and ongoing collaboration with hardware, software, and QA teams. Many companies provide ongoing training and encourage knowledge sharing within engineering teams to help individuals stay current and effectively tackle new technical hurdles. Overcoming these challenges not only sharpens technical expertise but also opens doors for career growth into architect, team lead, or principal engineer roles.

What are the most commonly searched types of Gpu Engineer jobs in Arizona? The most popular types of Gpu Engineer jobs in Arizona are:
What are popular job titles related to Gpu Engineer jobs in Arizona? For Gpu Engineer jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Gpu Engineer jobs in Arizona look for? The top searched job categories for Gpu Engineer jobs in Arizona are:
Gpu Engineer jobs near you
Infographic showing various Gpu Engineer job openings in Arizona as of July 2026, with employment types broken down into 100% Full Time. Highlights an 60% In-person, and 40% Remote job distribution, with an average salary of $94,822 per year, or $45.6 per hour.

ML Infrastructure Engineer

Bright Vision Technologies

Scottsdale, AZ • On-site, Remote

$100K - $150K/yr

Full-time

Posted 7 days ago

Job description

ML Infrastructure Engineer - Remote 
 
Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. 
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. 
 
Job Title: ML Infrastructure Engineer
Location: 100% Remote (U.S.) 
Position Type: Full-time, Direct W2 
Salary Range: $100,000–$150,000 Annually 
Experience Required: 6+ years 
 
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. 
 
Job Summary 
We are seeking an AI Infrastructure Engineer to design, build, and operate the platform layer that powers large-scale AI training and inference workloads. The role focuses on GPU clusters, distributed training frameworks, scheduling, storage performance, and developer experience for ML engineers and researchers, with strong emphasis on reliability, efficiency, and cost control. The ideal candidate has built or operated production AI infrastructure at scale, understands the interaction between hardware, kernel, scheduler, and ML framework, and brings strong software engineering discipline to platform work. 
Key Responsibilities 
  • Design and operate GPU and accelerator infrastructure for training and inference, spanning on-prem clusters, cloud-managed services, and hybrid configurations. 
  • Build scheduling, queueing, and resource-sharing systems that maximize accelerator utilization across many teams. 
  • Integrate frameworks such as PyTorch, JAX, DeepSpeed, FSDP, Megatron-LM, and Ray Train into a unified platform offering. 
  • Operate high-performance storage systems and data pipelines that keep accelerators fed with training data at near-line-rate. 
  • Design networking architectures supporting RDMA, InfiniBand, NCCL, and high-bandwidth collective communication. 
  • Build observability for AI workloads including utilization, throughput, training stability, and failure-mode analytics. 
  • Implement checkpointing, restart, and fault-tolerance patterns for long-running training jobs at scale. 
  • Drive cost optimization across compute, storage, and networking through scheduling, spot capacity, and right-sizing. 
  • Develop developer tooling and paved-road workflows that let researchers launch experiments safely and efficiently. 
  • Partner with research and applied ML teams to plan capacity for upcoming training runs. 
  • Implement security controls, isolation, and access management for multi-tenant AI infrastructure. 
  • Drive automation across cluster provisioning, lifecycle management, and configuration enforcement. 
  • Maintain runbooks, capacity dashboards, and operational documentation for the AI platform. 
  • Stay current with AI infrastructure research, accelerator hardware, and emerging open-source AI tooling. 
Required Qualifications 
  • Bachelor’s or Master’s degree in Computer Science or a related field. 
  • Six or more years of experience in infrastructure, platform, or HPC engineering. 
  • Hands-on experience operating GPU clusters or large-scale ML training infrastructure. 
  • Strong proficiency in Python and at least one systems language such as Go or C++. 
  • Deep understanding of distributed training, accelerator architectures, and collective communication. 
  • Experience with Kubernetes, Slurm, Ray, or similar scheduling systems for ML workloads. 
  • Strong understanding of Linux internals, networking, and high-performance storage. 
  • Experience with at least one major cloud provider’s ML infrastructure offerings. 
  • Strong software engineering practices including testing, CI/CD, and code review. 
  • Excellent communication and cross-functional collaboration skills. 
Preferred Qualifications 
  • Experience operating InfiniBand or RDMA networking at scale. 
  • Contributions to open-source ML infrastructure projects. 
  • Familiarity with custom orchestrators or research-grade training stacks. 
  • Exposure to frontier model training operations. 
  • Experience with FinOps for AI workloads. 
How to Apply 
Would you like to know more about this opportunity? For immediate consideration, please send your resume to Jenny@bvteck.com or contact us at (908) 505-3544. Learn more about Bright Vision Technologies at www.bvteck.com.
Bright Vision Technologies is an Equal Opportunity Employer.
 

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees\' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

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