About the company
Riot Games was established in 2006 by entrepreneurial gamers who believe that player-focused game development can result in great games. In 2009, Riot released its debut title League of Legends to critical and player acclaim. As the most played PC game in the world, over 100 million play every month. Players form the foundation of our community and it’s for them that we continue to evolve and improve the League of Legends experience.
We’re looking for humble but ambitious, razor-sharp professionals who can teach us a thing or two. We promise to return the favor. Like us, you take play seriously; you’re passionate about games. We embrace those who see things differently, aren’t afraid to experiment, and who have a healthy disregard for constraints.
Responsibilities
- Design, implement, and evolve internal platform capabilities that make AI Efficiency services easier to build, ship, observe, secure, and operate
- Build and maintain self-service workflows, reusable platform abstractions, and golden paths that improve developer productivity while preserving reliability, security, and governance
- Improve platform reliability through better monitoring, alerting, observability, deployment safety, release practices, and incident readiness
- Define and operationalize service health indicators, SLIs, SLOs, and related reliability metrics that help teams make informed tradeoffs between reliability, velocity, and cost
- Build automation that reduces operational toil and improves mean time to detect, respond, and recover from incidents
- Partner with engineers throughout the software development lifecycle to embed operability, production readiness, and maintainability into system design, implementation, rollout, and ongoing support
- Improve CI/CD systems, developer workflows, and release pipelines so shipping becomes safer, faster, and more repeatable
- Identify platform and reliability risks across distributed systems, infrastructure, service dependencies, and operational workflows, and drive durable improvements
- Troubleshoot AI model-serving issues across frameworks, runtimes, and hardware environments, including diagnosing configuration, compatibility, and performance issues across different GPU platforms and supporting model format conversion workflows when needed
- Design and run resilience, recovery, and failure-mode testing to validate system behavior under stress and uncover hidden weaknesses before they impact users
- Evaluate, integrate, and operate AI-assisted engineering tools that improve code quality, reliability, security, performance, and developer productivity across the software delivery lifecycle
- Build and evolve automation pipelines that combine conventional CI/CD systems with agentic workflows such as automated code review, bug detection, regression analysis, test generation, remediation suggestions, and workflow verification
- Partner with engineers to introduce safe, auditable, and measurable uses of AI agents in areas such as pull request review, operational diagnostics, UI and UX validation, accessibility checks, and production readiness checks
- Define guardrails, approval workflows, observability, reporting, and escalation paths for AI-assisted automation to ensure these systems remain safe, trustworthy, and operationally effective
- Establish evaluation frameworks and success metrics for AI-native development tooling, including quality lift, false positive rates, latency, cost, operational risk, and impact on engineering throughput
- Lead or contribute to incident response and post-incident improvement work for critical internal platforms and services, with a focus on systemic fixes and long-term resilience