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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - Infrastructure (Technical Leadership) - **Company:** The Meta Game, Inc. - **Location:** New York, NY, United States - **Salary:** $271,000.0 - $347,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, C++ (Programming Language), Computer Engineering, Software Debugging, Distributed Systems, Python (Programming Language), Routing, Operational Databases, Performance Tuning, Inference Optimization, Software Deployment, Software Engineering, Reinforcement Learning, Rust (Programming Language), Prompt Engineering, Agentic-AI, Information Technology, Machine Learning Operations, Virtual Agents - **Published:** October 16, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=3deb61399c2c7db1 ## About the Role * 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, * 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 ## Description sound judgment about when and how to apply AI to production problems, a long-term technical vision, and the ability to move quickly from an ambitious idea to a production system operating safely at Meta scale.This is also a deeply hands-on senior IC role. You will personally prototype, build, evaluate, and ship AI systems, working directly in the code and with production data. A central challenge is creating systems that improve from experience: learning from outcomes, identifying their own failure modes, testing changes, and safely increasing their effectiveness over time., * 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