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

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. Team - Creator Services Machine Intelligence Team: The Machine Intelligence team is building an NPC system that can (1) play any Roblox game and (2) perform real-time inference efficiently enough to support deployment to all Roblox players. - ML Platform Team: The Foundation AI Group is on a mission to establish Roblox as the standard for 3D foundational models (3DFMs), democratizing creation by making it simple for anyone to generate high-quality, immersive 3D experiences using AI. The AI Platform team is a foundational part of this vision, supporting hundreds of ML use cases and billions of inferences daily across Discovery, Safety, Engine, and more. We are seeking exceptional PhD new graduates to drive innovation across three critical areas: AI Platform, Distributed Inference Systems. What You Will Do As a Senior Machine Learning Engineer, you will be a key contributor to building the cutting-edge systems that power AI at Roblox. Creator Services Machine Intelligence Team - 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. ML Platform Team Track 1: AI Platform Projects - 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. Track 2: Distributed Inference & Systems Optimization - 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. You Have - 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). For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page. Annual Salary Range $196,750—$243,290 USD Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted). Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox

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).

Senior Machine Learning Engineer responsibilities include: For Creator Services Machine Intelligence Team — develop scale data pipelines to collect complex 3D game states and real-time player actions; train novel architectures by solving feature extraction across games for NPC models and improve training speed for advanced deep learning architectures; optimize real-time inference to support the deployment of tens to hundreds of autonomous NPCs in real-time environments. For ML Platform Team — Pioneer next-generation AI tooling to improve efficiency, cost, and usability of ML@Roblox; build and maintain core platform components (Serving Layer, Model Registry, Pipeline Orchestrator, Training/Inference control planes); design developer experiences to reduce time-to-production and ensure scalable AI systems; in Distributed Inference & Systems Optimization, architect scalable distributed inference systems for serving LLMs and Large Recommender Models at massive scale; optimize inference engine for millions of QPS at low latency; perform deep, low-level performance analysis and optimize ML models and systems on GPU architectures using techniques like continuous batching, speculative decoding, and quantization.