Principal AI Reliability Engineer

Meta; Salem (OR); On-site; USD 271,000 - 347,000; Full time

Job summary

Meta is seeking an experienced Software Engineer to build AI systems for production reliability, intersecting distributed systems, incident response, and applied AI. Lead development of an autonomous AI agent to investigate incidents, identify root causes, and execute safe mitigation under supervision, advancing agentic capabilities at Meta scale.

The role blends infrastructure expertise with pragmatic AI deployment, requiring hands-on engineering, prototype to production, and cross-team

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 12+ years of software eng experience in large-scale distributed or infrastructure systems.
• Experience leading multi-year engineering efforts across organizations.
• Experience applying AI/ML systems to production problems.

Responsibilities

• Define the technical vision and architecture for agentic reliability systems.
• Design, code, and ship production agentic systems for infrastructure problems.
• Lead development of an AI agent for production incident investigation and mitigation.
• Develop improvements in agent reasoning, context, and safe execution.
• Apply techniques like fine‑tuning, RL, distillation, and model serving optimization.
• Identify new AI applications across incident prevention, detection, mitigation, and observability.

Skills

AI systems

Distributed systems

Incident response

Python

C++

Java

Rust

Production systems

Telemetry

Model serving

Education

Bachelor's degree in CS/Engineering

Tools

Git

Job description

Meta is seeking an experienced Software Engineer to build AI systems for production reliability, intersecting distributed systems, incident response, and applied AI. Lead development of an autonomous AI agent to investigate incidents, identify root causes, and execute safe mitigation under supervision, advancing agentic capabilities at Meta scale.

The role blends infrastructure expertise with pragmatic AI deployment, requiring hands-on engineering, prototype to production, and cross-team