Staff Machine Learning Engineer - Game Tech Group, ML Platform

Riot Games•Los Angeles, CA

10d•$229,200 - $319,500

About The Position

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 with a clear illustration of extraordinary engineering. 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 AI systems across Riot. The ML Platform team builds and operates the shared infrastructure behind Riot’s AI ecosystem, including model serving, orchestration, feature management, and deployment workflows. Our goal is to help teams across Riot move ML systems from experimentation into reliable production services quickly and confidently. As a Staff Machine Learning Engineer on the ML Platform team, you will architect systems for model deployment, observability, and lifecycle management while helping shape the long-term direction of Riot’s ML platform. You will apply modern MLOps practices to improve reliability, scalability, and developer experience for teams building AI-powered products across Riot. You will report to the Engineering Manager.

Requirements

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

Nice To Haves

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
Embody player empathy and care about players' experiences

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