ZipRecruiter

Log In

1

Ml Infrastructure Engineer Jobs in Arizona (NOW HIRING)

Programmers.io

AI/ML Engineer

Scottsdale, AZ · On-site

Manage and optimize cloud-based ML infrastructure (GCP Vertex AI, AWS SageMaker, or equivalent ... Required Qualifications: 5 years experience in DevOps, CloudOps, or ML Ops. 5 years experience with ...

PNC Bank

Infrastructure Engineer - Db2 Support

Phoenix, AZ · On-site

$86K - $172K/yr

As an Infrastructure Engineer you will be required to install, configure, test & deploy mainframe ... ML), Mainframe Technologies, Risk Assessments, Team Player, Technical Knowledge Competencies ...

IntraEdge

Senior AI/ML & IVR Engineer GCP

Scottsdale, AZ · On-site

$105K - $145K/yr

As a Senior AI/ML, IVR, and GCP Engineer, you will architect, develop, and optimize advanced AI/ML ... Knowledge of infrastructure-as-code (Terraform, Cloud Deployment Manager) Success in This Role:

Inficare Technologies

Lead AI Engineer

Phoenix, AZ · On-site

$99K - $131K/yr

... ML infrastructure * Proven ownership of complex, cross-cutting agentic systems spanning multiple ... Strong engineering fundamentals across backend systems, APIs, data pipelines, and cloud ...

Orbotech

Full Stack AI Engineer

Phoenix, AZ · On-site

You will work alongside data scientists and full-stack engineers to deliver AI-powered solutions across internal tooling, customer-facing features, and the supporting data/ML infrastructure. The team ...

KLA Corporation

Full Stack AI Engineer

Chandler, AZ · On-site

You will work alongside data scientists and full-stack engineers to deliver AI-powered solutions across internal tooling, customer-facing features, and the supporting data/ML infrastructure. The team ...

next page

Showing results 1-20

All JobsMl Infrastructure Engineer JobsMl Infrastructure Engineer Jobs in Arizona

Ml Infrastructure Engineer information

See Arizona salary details

$43.3K

$118.4K

$169.6K

How much do ml infrastructure engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for ml infrastructure engineer in Arizona is $118,411.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,200.00 and $131,400.00 per year, depending on experience, location, and employer.

What is the difference between Ml Infrastructure Engineer vs Data Engineer?

AspectML Infrastructure EngineerData Engineer
Required CredentialsBachelor's/Master's in CS, experience with cloud platforms, scripting, and ML toolsBachelor's/Master's in CS, experience with databases, ETL, and data pipelines
Work EnvironmentFocus on deploying and maintaining ML systems, cloud infrastructure, and automationDesigning and building data pipelines, managing large datasets, and data storage
Employer & Industry UsageTech companies, AI startups, research labsFinance, healthcare, e-commerce, and data-driven industries

The ML Infrastructure Engineer specializes in building and maintaining the infrastructure that supports machine learning models, focusing on deployment, scalability, and automation. In contrast, Data Engineers primarily develop data pipelines and manage large datasets to enable data analysis and business intelligence. Both roles require strong technical skills and often overlap, but their core focus areas differ significantly.

What are popular job titles related to Ml Infrastructure Engineer jobs in Arizona?

For Ml Infrastructure Engineer jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Ml Infrastructure Engineer jobs in Arizona look for?

The top searched job categories for Ml Infrastructure Engineer jobs in Arizona are:

What cities in Arizona are hiring for Ml Infrastructure Engineer jobs?

Cities in Arizona with the most Ml Infrastructure Engineer job openings:

Ml Infrastructure Engineer jobs near you
Infographic showing various Ml Infrastructure Engineer job openings in Arizona as of August 2026, with employment types broken down into 92% Full Time, 3% Part Time, and 5% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $118,411 per year, or $56.9 per hour.

ML Infrastructure Engineer

Bright Vision Technologies

Phoenix, AZ • On-site, Remote

$150K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

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.

Apply