Riot Games is seeking a Software Engineer to help build and scale their next-generation ML Platform, enabling productionized machine learning across all of Riot at a global scale by applying modern MLOps practices.
Requirements
- Experience with cloud-native systems (e.g., Kubernetes, containers, autoscaling, observability).
- Experience with one or more inference serving frameworks (e.g., NVIDIA Triton, KServe, TorchServe, BentoML, Seldon Core etc)
- Familiarity with GPU orchestration, performance tuning, and cost-aware scheduling
- Exposure to CI/CD workflows, infrastructure-as-code (e.g., Terraform), and artifact management.
- Proficiency in Python and experience with package management tools (e.g., Conda, Poetry).
- Familiarity with ML workflow tools (MLFlow, DVC, LakeFS, etc) and drift monitoring strategies
- Exposure to AB testing and experimentation frameworks, especially in online model evaluation
Responsibilities
- Implement and support ML inference infrastructure for real-time and batch serving, including deployment pipelines and CPU/GPU-aware orchestration.
- Contribute to CI/CD workflows for ML artifacts, helping enable rapid iteration and safe promotion from development to production.
- Develop and maintain tooling for environment and dependency management (e.g., Conda/Poetry lock files, secure image builds) to ensure reproducible ML runtimes.
- Implement platform observability features such as monitoring, drift detection, resource utilization, and latency tracking.
- Support ML deployment best practices**,** including multi-version models, blue/green rollouts, shadow deployments, and safe rollback strategies.
- Collaborate on long-term platform architecture**,** providing input into design decisions and contributing to team discussions.
- Contribute upstream to shared infra initiatives and build a feedback loops and collaboration models with other Riot platform teams
Other
- 2+ years of experience in software engineering, with time spent on ML/AI, platform or infrastructure teams.
- Interest in MLOps and machine learning platforms, with a desire to grow technical depth in this space.
- Familiarity with machine learning workflows (e.g., training, validation, deployment, monitoring) and experience working with data scientists.
- Passion for player experience, game systems, or creative technology development
- craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players.
