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Ai Infrastructure Jobs in Portland, OR (NOW HIRING)

Intel

AI Infrastructure Engineer

Hillsboro, OR · On-site

$170K - $315K/yr

We are looking for a performance-obsessed AI Infrastructure Engineer to push LLM inference to its absolute limits on Intel's next-generation GPU architectures. In this role, you will dive deep into ...

Intel

Data Center and AI Brand Strategist

Hillsboro, OR · On-site

Working technical fluency in data center and AI infrastructure - a clear understanding of how CPUs, GPUs, and AI accelerators differ and complement each other, how heterogeneous compute systems are ...

Embedded Shishya

AI Context Operations Lead

Portland, OR · On-site

$163 - $203.80/hr

AI Ops builds the systems that keep that organization moving, helping teams move quickly without ... In this role, you'll own Mercury's internal knowledge infrastructure: the systems and standards ...

Nvidia

Senior Applied AI Engineer

Hillsboro, OR · On-site

$113K - $156K/yr

Design, develop, and improve scalable infrastructure to support the next generation of AI ... applications, including copilots and agentic tools. * Drive improvements in architecture ...

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All JobsAi Infrastructure JobsAi Infrastructure Jobs in Portland, OR

Ai Infrastructure information

See Portland, OR salary details

$29

$62

$92

How much do ai infrastructure jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for ai infrastructure in Portland, OR is $62.77, according to ZipRecruiter salary data. Most workers in this role earn between $50.96 and $73.17 per hour, depending on experience, location, and employer.

What is AI infrastructure?

AI infrastructure refers to the combination of hardware, software, and cloud-based solutions that support the development, deployment, and scaling of artificial intelligence applications. It includes components such as GPUs, CPUs, storage systems, networking, data management tools, and machine learning frameworks. The goal of AI infrastructure is to provide the computational power and resources needed to train, test, and run AI models efficiently, whether on-premises or in the cloud. Organizations invest in robust AI infrastructure to accelerate innovation, manage large datasets, and ensure the reliability of their AI systems.

What are the key skills and qualifications needed to thrive in AI infrastructure?

To thrive in AI Infrastructure, you need expertise in software engineering, distributed systems, cloud platforms, and a solid understanding of machine learning workflows, often supported by degrees in computer science or related fields. Familiarity with tools like Kubernetes, Docker, Terraform, and cloud services (AWS, GCP, Azure), as well as experience with CI/CD pipelines and monitoring systems, is essential. Strong problem-solving abilities, effective communication, and adaptability help professionals excel in cross-functional teams and rapidly evolving environments. These skills and qualities are crucial for building scalable, reliable systems that power AI applications and support organizational innovation.

What are common challenges faced by professionals working in AI infrastructure roles, and how can they be addressed?

Professionals in AI Infrastructure roles often encounter challenges related to scalability, system reliability, and integration with existing IT environments. Managing rapidly growing datasets and ensuring seamless deployment of machine learning models can be complex, requiring robust automation and monitoring tools. Collaboration with data scientists, software engineers, and DevOps teams is critical to ensure infrastructure meets the evolving needs of AI projects. Staying updated with the latest cloud technologies and best practices can help address these challenges and drive successful AI implementations.

What is the difference between Ai Infrastructure vs Data Engineer?

AspectAi InfrastructureData Engineer
Required CredentialsBachelor's in CS, Engineering, or related; knowledge of cloud platforms and AI toolsBachelor's in CS, Data Science, or related; programming and database skills
Work EnvironmentCloud environments, AI model deployment, infrastructure setupData pipelines, database management, data processing
Employer & Industry UsageTech companies, AI startups, cloud providersTech firms, finance, healthcare, e-commerce

Ai Infrastructure professionals focus on building and maintaining the hardware and software systems that support AI models, while Data Engineers develop and manage data pipelines and databases. Both roles require technical skills and often collaborate but serve different core functions within AI and data ecosystems.

What are AI infrastructure jobs?

AI infrastructure jobs involve designing, building, and maintaining the hardware, software, and network systems necessary to support artificial intelligence applications. These roles often require knowledge of cloud computing, data centers, machine learning frameworks, and system optimization to ensure reliable and efficient AI model deployment and operation.

What are popular job titles related to Ai Infrastructure jobs in Portland, OR?

For Ai Infrastructure jobs in Portland, OR, the most frequently searched job titles are:

What job categories do people searching Ai Infrastructure jobs in Portland, OR look for?

The top searched job categories for Ai Infrastructure jobs in Portland, OR are:

What cities near Portland, OR are hiring for Ai Infrastructure jobs?

Cities near Portland, OR with the most Ai Infrastructure job openings:

Ai Infrastructure jobs near you
Infographic showing various Ai Infrastructure job openings in Portland, OR as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 70% Physical, 4% Hybrid, and 26% Remote job distribution, with an average salary of $130,552 per year, or $62.8 per hour.

AI Infrastructure Engineer

Intel

Hillsboro, OR • On-site

$170K - $315K/yr

Full-time

Medical, Retirement, PTO

Posted 29 days ago

Key responsibilities

  • Own the end-to-end optimization pipeline for running large language models on Intel GPUs.

  • Profile, diagnose, and resolve cross-stack performance bottlenecks, and develop custom GPU kernels for critical attention mechanisms and other components.

  • Upstream architectural improvements and hardware backends into open-source inference frameworks and collaborate with hardware and compiler teams to shape future GPU roadmaps.

Intel rating

8.7

Company rating: 8.7 out of 10

Based on 147 frontline employees who took The Breakroom Quiz

18th of 161 rated electronics manufacturers

Job description

Job Details:Job Description: 

We are looking for a performance-obsessed AI Infrastructure Engineer to push LLM inference to its absolute limits on Intel's next-generation GPU architectures.
In this role, you will dive deep into the inference stack and redefine peak performance. You will work end-to-end across the stack: profiling bottlenecks, writing custom GPU kernels, and upstreaming your optimizations directly into industry-standard serving frameworks like vLLM and SGLang. Your optimizations will be instrumental in unlocking the full potential of Intel hardware for state-of-the-art generative AI workloads.
What You Will Do
Drive Inference Performance: Own the end-to-end optimization pipeline for running state-of-the-art LLMs on Intel GPUs.
Deep Stack Optimization: Profile, diagnose, and resolve cross-stack performance bottlenecks.
Kernel Development and Integration: Design, write, and optimize custom high-performance kernels for critical attention mechanisms, MoE, quantization, and operator fusions.
Open Source Leadership: Upstream your architectural improvements and hardware backends directly into open-source repositories like vLLM, SGLang, and PyTorch, acting as a bridge between the hardware teams and the open-source community.
Shape the Hardware Roadmap: Apply roofline analysis and systematic profiling to decompose bottlenecks. You will partner with our architecture and compiler teams to shape future GPU roadmaps based on real-world GenAI workload data.

Show passion about AI infrastructure and performance optimization.

Qualifications:

Minimum Qualifications

Bachelors Degree in Computer Science, Software Engineering, Artificial Intelligence/Machine Learning, or related field and 4+ years experience, Masters Degree and 3+ years, OR PhD.
3+ years of relevant software engineering experience in GPU computing, AI systems, or high-performance computing (HPC).
Proficiency in modern C++ and Python. You are comfortable reading and modifying complex systems-level code.

Preferred Qualifications
Understanding of CPU/GPU architecture.
Understanding of modern LLM architectures and inference paradigms: attention mechanisms, KV caching, continuous batching, speculative decoding, and prefill-decode disaggregation.
Prior open-source contributions to inference engines (vLLM, SGLang, PyTorch, llama.cpp).
Hands-on experience writing and optimizing custom GPU kernels using Triton, SYCL, CUDA/CUTLASS, or other DSLs.
Experience with scale-out inference orchestration across multi-node topologies.
You leverage AI coding agents daily to accelerate your own workflow and benchmark generation.
Your expertise will play a vital role in advancing Intel's AI technology. We invite you to bring your skills, experience, and passion for AI to make an impact-apply today.

Job Type:Experienced HireShift:Shift 1 (United States of America)Primary Location: US, California, Santa ClaraAdditional Locations:US, California, Folsom, US, Oregon, Hillsboro, US, Texas, AustinPosting Statement:All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.Position of TrustN/ABenefits

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel.

Annual Salary Range for jobs which could be performed in the US: $170,500.00-315,490.00 USDThe range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific compensation range for your preferred location during the hiring process.

Work Model for this Role

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.

*

ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.

What Intel employees say

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About Intel

Sourced by ZipRecruiter

Intel strives to make every facet of semiconductor manufacturing state-of-the-art -- from semiconductor process development and manufacturing, through yield improvement to packaging, final test and optimization, and world class Supply Chain and facilities support. Employees in the Technology and Manufacturing Group are part of a worldwide network of design, development, manufacturing, and assembly/test facilities, all focused on utilizing the power of Moore's Law to bring smart, connected devices to every person on Earth

Industry

Manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1968

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