[2026] Senior Machine Learning Engineer (Systems), Embodied AI/NPCs, ML Platform - PhD Early Career

Roblox•San Mateo, CA

6d•$196,750 - $243,290•Onsite

About The Position

Roblox is building the tools and platform that empower its community to bring any experience they can imagine to life. The Creator Services Machine Intelligence Team is building an NPC system that can play any Roblox game and perform real-time inference efficiently enough to support deployment to all Roblox players. The ML Platform Team, within the Foundation AI Group, is establishing Roblox as the standard for 3D foundational models, democratizing creation using AI. This role is for exceptional PhD new graduates to drive innovation across AI Platform and Distributed Inference Systems. As a Senior Machine Learning Engineer, you will be a key contributor to building the cutting-edge systems that power AI at Roblox. This involves developing scale data pipelines, training novel architectures, optimizing real-time inference, pioneering next-generation AI tooling, building core platform components, designing great developer experiences, and architecting scalable distributed inference systems for LLMs and Large Recommender Models.

Requirements

Possessing or pursuing a Ph.D. in Computer Science, Computer Engineering, Mathematics, Statistics, or a related technical field, with a thesis aligned to Roblox’s research areas.
Built end-to-end ML pipelines and managed model inference and deployment.
Experience with novel datasets, and building real-world agentic applications.
Scaled high-performance, high-availability architectures.
Handled infrastructure using Kubernetes and major cloud providers (AWS, Azure, or GCP).

Responsibilities

Develop Scale Data Pipelines: Design, build and maintain robust data pipelines to collect complex 3D game states and real-time player actions across the platform.
Train Novel Architectures: Solve the feature extraction across games for NPC model in a general and scalable way and drive model training speed for novel, sophisticated deep learning architectures.
Optimize Real-Time Inference: Engineer high-performance model inference solutions to support the seamless deployment of 10s to 100s of autonomous NPCs in real-time environments.
Pioneer next-generation AI tooling to enhance the efficiency, cost, and usability of ML@Roblox.
Build and maintain core platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes.
Design great developer experiences (paved-road templates, tooling, visualizations) to reduce time-to-production and ensure foundational AI systems are scalable and reliable.
Architect and implement scalable distributed inference systems for efficiently serving LLMs and Large Recommender Models at massive scale.
Optimize our inference engine to serve millions of QPS at low latency.
Conduct deep, low-level performance analysis and optimize ML models (using techniques like continuous batching, speculative decoding, and quantization) and systems on GPU architectures to maintain peak performance and stability.