[2026] Senior Machine Learning Engineer, AI Platform - PhD Early Career

Roblox is a platform where millions of people explore, create, and connect in 3D immersive digital experiences. They are seeking a Senior Machine Learning Engineer for their AI Platform team to contribute to building cutting-edge systems that power AI, focusing on AI tooling, distributed inference systems, and generative AI information retrieval.

Responsibilities

• 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
• 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
• Lead the design and development of Retrieval-Augmented Generation (RAG) systems
• Build and maintain core information retrieval infrastructure—vector databases and knowledge graphs—to enable accurate grounding of Gen AI models
• Ship language models and 3D objects as a service for the Roblox community, making creation easier

Skills

• 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
• Experience with high performance distributed systems, ML Infrastructure, LLM fine tuning/RL, Information Retrieval and Gen AI context generation
• Expertise in one or more of the following key areas: AI/ML Platform Data stores - Features stores, Vector DBs and Knowledge Graphs
• Expertise in LLMs - Fine tuning, Safety
• Expertise in Agentic systems - Agent evaluation, context engineering
• Experience building agentic applications with context for real world applications
• Collaborative mindset and experience integrating and deploying optimized models with cross-functional teams, including data scientists and software engineers
• Experience with graph databases and large-scale GNNs (Graph Neural Networks)
• Experience working with Kubernetes
• Experience working with one or more cloud providers (e.g., AWS, Azure, GCP)
• Experience working with high availability systems
• Experience working with ML models, LLMs or other AI systems

Benefits

• Equity compensation
• Benefits as described on this page

Company Overview

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