ZipRecruiter

Log In

1

Ai Infrastructure Engineer Jobs in California (NOW HIRING)

Tencent

Sr. Cloud AI Infrastructure Engineer

Palo Alto, CA · On-site

$127K - $173K/yr

Tencent is a leading technology company seeking a Sr. Cloud AI Infrastructure Engineer. This role involves conducting architecture research, optimizing performance for large-scale cloud computing ...

NIO

Sr/Staff AI Infrastructure Engineer

San Jose, CA

$163K - $212K/yr

Partner with engineering teams to understand real-world constraints and to support the high-quality ... AI extension/application/project * Experience with cloud infrastructure and training (Azure, AWS ...

Ghost

Infrastructure Engineer

Los Angeles, CA · On-site

$140K - $160K/yr

Company Overview Ghost is an AI-native inventory distribution platform and operating system for ... About the Role We're looking for an Infrastructure Engineer who loves building reliable, scalable ...

Ghost

Infrastructure Engineer

Los Angeles, CA · On-site

$140K - $160K/yr

Company Overview Ghost is an AI-native inventory distribution platform and operating system for ... About the Role We're looking for an Infrastructure Engineer who loves building reliable, scalable ...

previous pagenext page

Showing results 21-40

All JobsAi Infrastructure Engineer JobsAi Infrastructure Engineer Jobs in California

Ai Infrastructure Engineer information

See California salary details

$45.9K

$125.4K

$179.6K

How much do ai infrastructure engineer jobs pay per year?

As of Aug 13, 2026, the average yearly pay for ai infrastructure engineer in California is $125,402.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,100.00 and $139,200.00 per year, depending on experience, location, and employer.

How much do AI infrastructure engineers make?

AI infrastructure engineers typically earn between $100,000 and $150,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in cloud platforms and hardware may earn higher salaries, often exceeding $180,000.

What does a typical day look like for an AI infrastructure engineer?

A typical day for an AI Infrastructure Engineer often involves designing and maintaining the underlying systems that support machine learning and AI workloads, such as setting up scalable cloud environments, automating workflows with CI/CD pipelines, and troubleshooting performance bottlenecks. You might collaborate closely with data scientists and software engineers to ensure seamless integration between AI models and production infrastructure. Daily activities can include writing and reviewing infrastructure-as-code, monitoring system health, and responding to incidents or scaling requests as needed. This role offers a dynamic mix of hands-on technical work, problem-solving, and teamwork, providing opportunities to refine your skills and contribute meaningfully to cutting-edge AI projects.

What are the key skills and qualifications needed to thrive as an AI infrastructure engineer?

To thrive as an AI Infrastructure Engineer, a strong background in computer science, cloud computing, and distributed systems is typically required, often supported by a degree in a related field. Familiarity with tools like Kubernetes, Docker, TensorFlow, and cloud platforms (AWS, Azure, or GCP), along with certifications in cloud technologies or DevOps, is highly valuable. Strong problem-solving abilities, collaboration, and effective communication skills are essential to excel within multidisciplinary engineering teams. These competencies ensure the reliable deployment, scaling, and optimization of AI workloads in dynamic production environments.

What does an AI infrastructure engineer do?

An AI Infrastructure Engineer designs, builds, and maintains the computing systems that support AI and machine learning workloads. They manage cloud services, optimize hardware and software performance, and ensure scalability for AI models. Their work involves configuring GPUs, CPUs, storage, and networking, as well as automating workflows with DevOps and MLOps tools. They collaborate with data scientists and engineers to streamline AI development and deployment. Their goal is to create reliable, efficient, and scalable AI infrastructure.

What are the most commonly searched types of Ai Infrastructure Engineer jobs in California?

The most popular types of Ai Infrastructure Engineer jobs in California are:

What are popular job titles related to Ai Infrastructure Engineer jobs in California?

For Ai Infrastructure Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Ai Infrastructure Engineer jobs in California look for?

The top searched job categories for Ai Infrastructure Engineer jobs in California are:

What cities in California are hiring for Ai Infrastructure Engineer jobs?

Cities in California with the most Ai Infrastructure Engineer job openings:

Ai Infrastructure Engineer jobs near you
Infographic showing various Ai Infrastructure Engineer job openings in California as of August 2026, with employment types broken down into 76% Full Time, 19% Part Time, 2% Temporary, and 3% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $125,402 per year, or $60.3 per hour.

AI Cloud Infrastructure Engineer - Fury Team

Scout AI

Sunnyvale, CA • On-site

$125K - $164K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Summary:
Scout AI is developing Fury, the first robotic foundation model for defense, aiming to empower U.S. forces with intelligent machines. The AI Infrastructure Engineer will build and scale the infrastructure for model training and deployment, ensuring efficient operations across edge and cloud environments.
Responsibilities:
• Design and implement data pipelines for ingesting, transforming, and storing petabytes of multimodal data from Fury’s robotic and operator systems
• Develop internal tooling for dataset exploration, curation, versioning, and quality monitoring over time
• Build and maintain distributed training infrastructure (cloud and on-prem) for large-scale multimodal and foundation model training
• Implement job orchestration workflows for launching, tracking, and debugging large-scale model runs
• Identify and remediate bottlenecks in compute, memory, storage, and network performance to optimize throughput and cost efficiency
• Collaborate with AI, autonomy, and systems teams to ensure data and training infrastructure supports real-time and mission-critical use cases
• Maintain observability and reliability tooling for training and inference pipelines
• Stay current on best practices in MLOps, distributed training frameworks, and AI infrastructure at scale
Qualifications:
Required:
• 3+ years of experience in ML infrastructure, MLOps, or large-scale data systems
• Proven experience with distributed training (PyTorch DDP, DeepSpeed, Ray, or similar) and workflow orchestration (Kubernetes, Airflow, or equivalent)
• Strong proficiency in Python and cloud-native infrastructure (AWS, GCP, or Azure)
• Deep understanding of data engineering (ETL pipelines, object storage, data versioning, metadata management)
• Familiarity with containerization and deployment (Docker, Kubernetes) and monitoring systems (Prometheus, Grafana)
• Experience optimizing GPU cluster utilization, scaling training jobs, and profiling model performance
• Bachelor’s degree or higher in Computer Science, Electrical Engineering, or related technical field
• Must be a U.S. Person due to required access to U.S. export controlled information or facilities
Preferred:
• Experience with edge-deployed ML systems
• Experience with federated training
• Experience with robotic data collection pipelines
Company:
Scout AI develops artificial intelligence systems for defense robotics, focusing on enabling autonomous behavior across unmanned platforms. Founded in 2024, the company is headquartered in Sunnyvale, USA, with a team of 11-50 employees. The company is currently Early Stage.

Apply