Build scalable NPC and digital-player systems spanning game engine, backend, and ML pipelines

This role sits on Roblox's Creator Service NPC team, building full-stack systems for in-game NPCs and digital players using LLMs, imitation learning, reinforcement learning, and behavior trees. It requires 5+ years designing distributed backend systems, hands-on microservices and game engine experience, and comfort spanning client, engine, backend, and ML layers.

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What this interview tests

Full-stack systems spanning client, game engine, backend services, and ML models
Highly scalable, reliable distributed backend system design (microservices)
NPC/agent behavior techniques: LLMs, imitation learning, reinforcement learning, behavior trees, state machines
Data pipelines, feature transformation, and annotation tooling for ML
Inference setup within/on top of a game engine
Long-term architectural thinking and cross-functional execution with product/design

Common question themes

Describe a distributed backend system you designed and led at scale

How would you build a data pipeline to support training or evaluating NPC behavior models

Tell me about hands-on work integrating with a game engine

How do you decide between LLM-based, imitation learning, or behavior-tree approaches for an NPC

Describe a design decision that held up well multiple years later

How do you gather and act on creator/community feedback for a product area you own

How candidates describe it

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