[2026] Applied Scientist - PhD Intern

[2026] Applied Scientist - PhD Intern

San Mateo, CA, United StatesEarly CareerID: 5262

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.

Roblox is spearheading the future of immersive entertainment with the ambitious goal ofdominating 10% of the gaming market. Join us to tackle challenges that enable AI-driven creation and massive concurrency for a global community where innovation meets safety.

During this 12 or 16 week internship, you will drive a project from ideation to experimentation, production or publication, with the possibility of continued collaboration beyond the internship.Your project will focus on the hardest problems in tech today affecting massive scale with 112+ million daily active users. We support experimentation and production with top researchers, engineers, faculty, and PhD candidates globally that can lead to publications at esteemed venues.

Teams Hiring For This Role:

• Search and Discovery: powers major recommendation surfaces—conducting cutting-edge research in generative modeling, multimodal and MLLM technologies, designing advanced agentic AI algorithms to solve business requirements while achieving technical breakthroughs.
• Economy: Expand the ML backbone powering monetization, marketplace, and payments, solving problems related to fraud, pricing, and personalization
• Ads & Brands: Evolve the intersection of recommendation systems, ranking, and marketplace/auction theory to optimize sponsored content delivery
• Safety: Create scalable AI for real-time safety & moderation using LLMs, NLP, and computer vision to safeguard text, voice, and accounts.
• Engine: Develop innovative technology for our immersive 3D graphics, advancing geometry, physics simulation, motion diffusion models, and scalable distributed systems
• Creator: Empower creators with generative AI and NLP technologies, building intuitive tools for automated content understanding and real-time localization
• Foundational AI: Design and train 3D foundational models and optimize large-scale inference systems, enabling intelligent 3D content generation.
• Infrastructure: Build systems that ensure the reliability and scalability of Roblox’s datacenter, backbone, and edge networks, which support all of our production services.

You Will

• Prototype and experiment with advanced AI, machine learning, and 3D technologies to solve real-world challenges at scale
• Translate cutting-edge research into production-ready features by working alongside world class engineering and product teams
• Develop and deliver solutions that enhance user experience, safety, and creativity on a global platform

You Have

• Possessing or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.
• Deep expertise in a relevant technical domain such as deep learning, computer vision, NLP, reinforcement learning, or distributed systems.
• Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.
• A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues.

You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).