Principal Software Engineer, Systems

Responsibilities

• Define the technical vision and architecture for agentic reliability systems across Meta
• Personally design, code, and ship production agentic systems for complex infrastructure problems
• Lead the development of an AI agent for production incident investigation and mitigation, advancing it toward accurate, trusted, and safely supervised autonomous action
• Develop major improvements in agent reasoning, context, tool use, planning, evaluation, learning, and safe execution
• Explore and apply techniques including automated hill climbing, fine-tuning, reinforcement learning, model routing, distillation, pruning, and inference optimization, selecting the simplest approach that produces measurable gains
• Identify new high-value applications of AI across incident prevention, detection, mitigation, observability, and infrastructure operations
• Move rapidly from ambiguous problems to prototypes, validate them against real production workloads, and develop successful approaches into reliable systems at scale
• Build evaluation and experimentation systems that connect agent quality to outcomes such as investigation accuracy, successful mitigation, incident duration, and reduced operational work
• Build closed-loop improvement systems that turn production outcomes into evaluations, experiments, and better agent behavior
• Establish architectures and guardrails for production actions, including authorization, independent validation, auditability, rollback, and human oversight
• Partner with Infrastructure, AI, Product, and Reliability leaders to integrate agentic capabilities into Meta’s production ecosystem
• Influence technical strategy across organizations and mentor other engineers working on distributed systems and applied AI

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 12+ years of software engineering experience, including experience building and operating large-scale distributed or infrastructure systems
• Experience setting technical direction and leading complex, multi-year engineering efforts across organizational boundaries
• Experience applying AI or machine learning systems to production problems
• Demonstrated experience moving from technical concept to production deployment and measurable impact
• Experience diagnosing complex production systems using telemetry, code, configuration, and dependency information
• Experience coding in languages such as C++, Java, Python, Rust, or equivalent
• Recent hands-on experience building and shipping complex production systems, with the ability to move directly between architecture, experimentation, debugging, and implementation
• Experience influencing senior engineers and leaders without direct organizational authority

Preferred Qualifications

• Experience with autonomous or semi-autonomous production actions and their safety, authorization, and rollback mechanisms
• Experience building systems that operate at hyperscale under strict reliability and latency requirements
• Experience improving agent quality through evaluation, context engineering, fine-tuning, reinforcement learning, distillation, or model-serving optimization
• Track record of identifying unconventional opportunities, rapidly prototyping solutions, and changing the technical direction of a large organization
• Deep expertise in observability, incident response, change safety, distributed systems, or production infrastructure
• Experience building self-improving or self-evolving systems, including automated experimentation, feedback loops, hill climbing, reinforcement learning, or recursive self-improvement
• Experience building production AI agents that reason across telemetry, code, configuration, deployments, and operational knowledge

Compensation

• $271,000/year - $347,000/year; Country: US; Bonus eligible; Equity eligible

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