[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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