About the Job
Riot engineers bring deep knowledge of specific technical areas but also value the opportunity to work in many broader domains. As a Staff Machine Learning Engineer at Riot, you’ll also dive into projects that focus on team cohesiveness and cross-team goals. You’ll lead without authority and provide other engineers ...
Riot Games is building the next generation of its ML Platform to support AI and machine learning systems across game development, player experiences, and internal tools. We’re looking for a Staff Machine Learning Engineer to help design and scale the infrastructure powering a wide range of ML workloads and evolving ...
Key Responsibilities
Design and operate AI & ML inference infrastructure, including deployment pipelines and CPU/GPU-aware orchestration
Develop CI/CD workflows that enable rapid iteration and safe promotion from development to production
Optimize infrastructure supporting varied model architectures, from foundation models to gradient boosted trees, for high throughput, low latency, and high availability
Establish and evolve ML deployment best practices, including multi-version models, blue/green rollouts, shadow deployments, and rollback strategies
Improve developer experience by reducing operational complexity and simplifying platform onboarding
Influence long-term platform architecture and help shape technical direction across Riot’s ML ecosystem
Collaborate with researchers and game teams to understand product needs and build reusable platform capabilities
Use modern AI-assisted development tools and workflows thoughtfully to accelerate iteration, while maintaining engineering quality and reliability
Required Skills & Abilities
6+ years of experience in engineering, with time spent on ML/AI, platform or infrastructure teams
Experience operating inference platforms such as KServe and production ML infrastructure including Feast, Milvus, or similar open-source systems
Experience with one or more inference serving frameworks, including NVIDIA Triton/Dynamo, TorchServe, or similar systems
Familiarity with GPU orchestration, performance tuning, and cost-aware scheduling
Experience with CI/CD workflows, infrastructure-as-code (e.g., Terraform), and artifact management
Experience building and operating services within distributed or service-oriented architectures
Experience building ML infrastructure within a real-time, or latency-sensitive environment
Hands-on experience with optimizing ML & AI deployments (LLMs, diffusion models, etc.) for throughput, latency and reliability
Familiarity with agentic workflows and orchestration frameworks for LLM-based systems
Passion for player experience, game systems, or creative technology development
For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes the delight of players. We will be looking at your past studies, experience, and your personal relationship with games. If you embody player empathy and care about players' experiences, this could be your role!
Qualifications
Experience:
6 years experience
