AI Infrastructure Engineer Jobs
AI Infrastructure Engineer jobs are open across cloud computing, enterprise technology, and AI-native companies, from entry-level to staff and principal engineer, with specializations in MLOps, distributed systems, and GPU cluster management. Find a role that fits below and apply directly.
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Showing 5 of 674+ AI Infrastructure Engineer jobs
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
- Experience with storage technology (e.g., block storage, object storage, file storage) and distributed file systems (e.g., Lustre used for AI workloads).
- Experience managing cloud storage solutions for data storage, retrieval, or AI/ML training data.
- Experience designing system architecture for enterprise computing environments supporting AI/ML workloads.
Preferred qualifications:
- Experience in designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
- Experience in compute technologies, storage, or hardware architecture.
- Experience with HPC systems hardware architecture, network interconnect technologies and topologies, and HPC storage file systems.
- Experience in relational Database Management System (RDBMS) kernel or RDBMS storage.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design high-performance distributed storage systems optimized for large-scale AI training workloads.
- Optimize data pipelines and storage I/O throughput to eliminate bottlenecks in machine learning training.
- Implement scalable caching strategies to accelerate data access for deep learning frameworks.
- Develop robust system monitoring and telemetry tools to track storage performance and reliability metrics.
- Architect fault-tolerant storage architectures that ensure high availability and data durability.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
- Experience with storage technology (e.g., block storage, object storage, file storage) and distributed file systems (e.g., Lustre used for AI workloads).
- Experience managing cloud storage solutions for data storage, retrieval, or AI/ML training data.
- Experience designing system architecture for enterprise computing environments supporting AI/ML workloads.
Preferred qualifications:
- Experience in designing, building, and operating large-scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
- Experience in compute technologies, storage, or hardware architecture.
- Experience with HPC systems hardware architecture, network interconnect technologies and topologies, and HPC storage file systems.
- Experience in relational Database Management System (RDBMS) kernel or RDBMS storage.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design high-performance distributed storage systems optimized for large-scale AI training workloads.
- Optimize data pipelines and storage I/O throughput to eliminate bottlenecks in machine learning training.
- Implement scalable caching strategies to accelerate data access for deep learning frameworks.
- Develop robust system monitoring and telemetry tools to track storage performance and reliability metrics.
- Architect fault-tolerant storage architectures that ensure high availability and data durability.
AI Infrastructure Engineer Jobs by Experience Level
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Find JobsAI Infrastructure Engineer Job Market
Who's Hiring
- Google35
- Booz Allen Hamilton22
- SpaceX21
- NVIDIA19
- JPMorganChase16
Top Industries Hiring
- Technology & Software34
- Education8
- Banking & Financial Services8
- Consulting & Professional Services8
- Investment & Asset Management7
What Employers Look For
The qualifications that appear most often in AI infrastructure engineer jobs.
- Proficiency with Kubernetes and container orchestration at production scale
- Hands-on experience with at least one major cloud platform such as AWS, GCP, or Azure
- Familiarity with ML training frameworks including PyTorch or TensorFlow
- Experience building or maintaining MLOps pipelines and model serving infrastructure
- Strong scripting ability in Python and infrastructure-as-code tools such as Terraform
- Bachelor's degree in computer science, engineering, or a related technical field
Tips for Your AI Infrastructure Engineer Job Search
Tailor your resume to the stack
AI infrastructure roles vary sharply by stack. Highlight Kubernetes, Ray, or Slurm based on what each job posting emphasizes. A resume that mirrors the exact orchestration tools and cloud platforms named in the description will clear automated screening far more reliably.
Quantify compute scale you have managed
Hiring teams want to know the scale you have operated at. Specify GPU cluster sizes, training job throughput, or cost reductions you drove. Vague claims about 'large-scale infrastructure' mean little without node counts, terabytes, or latency improvements attached.
Apply early to roles that fit
Migrate Mate lists ai infrastructure engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings that match your cloud depth
Many ai infrastructure engineer roles are written for a specific cloud. If your deep experience is on AWS with SageMaker and EC2 UltraClusters, filter for AWS-heavy teams rather than applying broadly. Mismatched cloud depth is a common reason for early rejections.
Prepare a system design answer for ML workloads
Almost every technical interview includes a distributed system design question specific to training or inference pipelines. Practice walking through fault tolerance, checkpointing strategy, and cost controls for a large model training job before you get on the call.
Negotiate with total cost of ownership in mind
When evaluating an offer, ask about the team's current infrastructure budget ownership, on-call rotation, and whether the role controls vendor contracts. These details reveal scope and advancement potential more clearly than the job title alone does.
AI Infrastructure Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ai infrastructure engineers?
The companies hiring the most ai infrastructure engineers right now include Google, Booz Allen Hamilton, and SpaceX, with the largest share of openings in California, New York, and Texas, based on current listings on Migrate Mate as of November 2026. Demand is concentrated at cloud providers, AI labs, and large enterprises building internal model training platforms.
How many ai infrastructure engineer jobs are remote?
About 60% of ai infrastructure engineer openings are fully remote or hybrid as of November 2026, reflecting strong employer flexibility for infrastructure work that does not require physical hardware access. Roles focused on cloud-native MLOps pipelines and model serving tend to be the most remote-friendly, while positions tied to on-premises GPU clusters or data center operations are more likely to require on-site presence.
How do you become an ai infrastructure engineer?
Start by building a strong foundation in systems programming, networking, and Linux administration, then layer on cloud platform certifications and hands-on work with Kubernetes and container runtimes. Contribute to open-source MLOps or distributed systems projects to demonstrate real workload experience. Move into roles as a cloud or DevOps engineer first, then specialize by taking on GPU provisioning, model training pipelines, or inference serving ownership within your team.
Can you get hired as an ai infrastructure engineer with little experience?
Yes, entry-level ai infrastructure engineer roles exist, particularly at companies scaling their ML platforms who need engineers to own infrastructure tooling under senior guidance. Focus on demonstrating hands-on familiarity with Kubernetes, cloud storage, and at least one ML framework through personal projects or academic work. Applying to teams that already have senior infrastructure engineers increases your odds of being hired to grow rather than to lead from day one.
What does the ai infrastructure engineer interview process look like?
Most ai infrastructure engineer interviews include a recruiter screen, a technical phone round covering distributed systems concepts or cloud architecture, and an on-site or virtual loop with a system design question focused on ML workloads, a coding or scripting exercise, and a cross-functional conversation about how you have partnered with ML engineers or data scientists. Some companies add a take-home infrastructure design exercise, particularly for senior roles where on-call ownership and cost management are central responsibilities.
Where can I find and apply to ai infrastructure engineer jobs?
You can find and apply to ai infrastructure engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Search for roles that match your stack and experience level, then apply directly to each listing that fits.
See All 674+ AI Infrastructure Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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