Senior Analyst, AI Workflows & Automation

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.

At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.

A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

As an AI Workflow Specialist on the Trust & Safety Operations team, you will be the bridge between product, operations, and other relevant teams. You will be required to create workflows, using AI to design, prototype, and scale high‑impact automations. You will translate operational pain points into well‑designed AI workflows, partner closely with product and engineering, and build the evaluation frameworks that ensure these automations are safe, reliable, and measurable.

You Will:

• Partner with the Subject matter experts across the Support Ops and Shared service teams to understand the current gap and brainstorm on the desired solution.
• Map and document existing workflows to identify high‑leverage automation opportunities and clearly define “current vs. future state.”
• Design AI‑augmented workflows that combine LLM agents, existing rules engines, and human review to improve speed, quality, and consistency
• Prototype and iterate AI solutions using internal and external (accepted) tooling (e.g., Claude and other LLMs, internal agentic/AI workflow platforms, and Builder AI code intelligence) to turn workflow designs into working agents or automations.
• Define success metrics and evaluation plans (e.g., automation rate, handle time reduction, error/rollback rate, reviewer satisfaction) and build or integrate with eval pipelines to measure agent performance over time.
• Partner with product, engineering, and operations to prioritize use cases, scope requirements, and align AI workflows with product roadmaps and control/compliance expectations.
• Operationalize launches: contribute to playbooks, runbooks, and training so that operations teams can use AI agents confidently and understand when and how to intervene or escalate.