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Ai Reliability Engineer Jobs in California (NOW HIRING)

Etched

Reliability Engineer

Cupertino, CA

$2.0K/mo

About Etched Etched is building AI chips that are hard-coded for individual model architectures ... Reliability Engineer We are seeking a skilled and detail-oriented Reliability Engineer to join our ...

Etched

Reliability Engineer

Cupertino, CA · On-site

$2.0K/mo

About Etched Etched is building AI chips that are hard-coded for individual model architectures ... Reliability Engineer We are seeking a skilled and detail-oriented Reliability Engineer to join our ...

Etched

Reliability Engineer

Cupertino, CA · On-site

$2.0K/mo

About Etched Etched is building AI chips that are hard-coded for individual model architectures ... Reliability Engineer We are seeking a skilled and detail-oriented Reliability Engineer to join our ...

Runloop AI, Inc

Site Reliability Engineer

San Francisco, CA · On-site

$150K - $250K/yr

About Runloop Runloop.ai is pioneering the next generation of infrastructure and orchestration to ... As a SRE, you'll be responsible for the reliability, observability, performance, and security of ...

Meta

Reliability Engineer

Sunnyvale, CA

$144K/yr

Meta is seeking a Product Reliability Engineer to drive the quality, durability, and long-term ... Demonstrated use of AI tools to redesign reliability data analysis workflows, accelerate failure ...

Openmind

Reliability Engineer

San Francisco, CA · On-site

$150 - $200/hr

Build fleet observability: telemetry ingestion, automated log collection, and AI-assisted scoring ... Experience in reliability engineering, test infrastructure, SRE, or simulation for autonomous ...

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Ai Reliability Engineer information

What is an AI reliability engineer?

AI Reliability Engineers are professionals responsible for ensuring that artificial intelligence systems function reliably, safely, and effectively over time. They work on monitoring AI models in production, identifying and mitigating potential failures, and improving the robustness of AI systems. Their tasks often include testing, validation, performance monitoring, and implementing best practices for maintaining AI infrastructure. By focusing on reliability, they help organizations deploy AI solutions that are dependable and trustworthy in real-world environments.

What are some common challenges AI reliability engineers face when ensuring model robustness in production environments?

Ai Reliability Engineers often encounter challenges such as monitoring AI model performance for drift or unexpected behavior, managing data quality issues, and implementing automated alerting systems for anomalies. In production, it's crucial to ensure that AI models operate consistently and remain reliable under varying conditions and data inputs. Collaborating closely with data scientists, software engineers, and DevOps teams is essential to address these challenges and to continuously improve model reliability and uptime.

What are the key skills and qualifications needed to thrive as an AI reliability engineer, and why are they important?

To thrive as an AI Reliability Engineer, you need a solid background in computer science or engineering, expertise in AI/ML concepts, and experience with software testing and reliability methodologies. Familiarity with tools like TensorFlow, PyTorch, CI/CD pipelines, and reliability testing frameworks, along with certifications in cloud platforms (e.g., AWS Certified Machine Learning), is highly valuable. Analytical thinking, problem-solving abilities, and strong collaboration skills set top performers apart in this role. These skills ensure robust, dependable AI systems that meet performance standards and maintain trust in critical applications.

What is the difference between Ai Reliability Engineer vs Data Scientist?

AspectAi Reliability EngineerData Scientist
Required CredentialsBachelor's or master's in CS, engineering, or related; certifications in AI/MLBachelor's or master's in CS, statistics, or related; certifications in data analysis or ML
Work EnvironmentTech companies, AI-focused teams, engineering departmentsResearch labs, tech firms, analytics teams
Employer & Industry UsageAI product development, machine learning systems, reliability testingData analysis, predictive modeling, business insights

While both roles involve AI and ML, Ai Reliability Engineers focus on ensuring AI system robustness and uptime, whereas Data Scientists analyze data to generate insights and models. The roles often collaborate but serve different primary functions within AI projects.

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

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

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

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

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

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

Ai Reliability Engineer jobs near you
Infographic showing various Ai Reliability Engineer job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 60% In-person, and 40% Remote job distribution.

Lead, AI Reliability & Monitoring Engineering (San Francisco)

Postman

San Francisco, CA • On-site

$119K - $150K/yr

Full-time

Re-posted 6 days ago

Job description

A leading tech company in San Francisco is seeking a Member of Technical Staff, AI Reliability & Monitoring Engineering Lead. This role involves developing reliability metrics for AI-driven services and implementing robust monitoring systems. The ideal candidate will have experience in AI reliability engineering and a strong background in cloud platforms and incident response. This position offers competitive compensation and a hybrid work model.
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