Senior Software Engineer, Riot Games

• Bachelor’s degree in Computer Science or a related field, or equivalent practical experience
• 3+ years of software engineering experience, with meaningful experience in infrastructure, platform engineering, or SRE roles
• Experience operating distributed systems in production and keeping them healthy under real load
• Strong experience with Kubernetes, AWS or GCP, infrastructure-as-code, CI/CD, deployment automation, and production tooling
• Experience with GPU compute infrastructure, including scheduling, multi-node orchestration, and resource optimization for long-running training workloads
• Proficiency in Python and solid understanding of networking, microservices, core infrastructure services, and distributed systems fundamentals

Desirable

• Platform and infrastructure engineers at Riot build the foundational systems that enable teams to develop, deploy, and operate systems at global scale
• They partner across disciplines with other engineers, data scientists, designers, and product teams to ensure reliable, scalable, and secure infrastructure underpins every ML capability that reaches players
• As a Senior Platform & Infrastructure Engineer on the Riot Technology team, you will design, build, and operate the core infrastructure and ML platforms
• Your focus will be on the computing and orchestration platforms that power large-scale distributed training of agents (e.g. via RL, IL, and other techniques), simulation environments, and policy evaluation, as well as the CI/CD, infrastructure-as-code, observability, and developer tooling that keep these systems production-grade
• You will close critical infrastructure gaps across the team's stack, driving improvements to standards, automation, and operational maturity
• You will operate independently on multi-month work efforts and begin to influence technical direction beyond your immediate team
• You will report to the Manager of Machine Learning
• Build and operate Kubernetes, multi-node GPU clusters, and networking infrastructure for distributed ML bot training and large-scale policy evaluation
• Design infrastructure for running simulation environments at scale, enabling parallel rollouts, data collection, training, and evaluation
• Build CI/CD, deployment automation, artifact management, and infrastructure-as-code across cloud environments
• Improve platform reliability, cost efficiency, performance, reproducibility, auditability, and operational maturity
• Build observability, monitoring, alerting, health indicators, and SLO-aligned dashboards for infrastructure and ML workloads
• Develop internal APIs, control planes, templates, and developer tooling for distributed training and evaluation workflows
• Support MLOps workflows including automated training pipelines, model artifact management, experiment tracking, and reproducible ML lifecycle operations
• Build security and governance controls, manage production incidents, drive root-cause remediation, mentor engineers, and support recruiting for platform roles