Distinguished Engineer, Machine Learning Systems - Economy (San Mateo)

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.At Roblox, we’re building the tools and platform that empower a global community of creators and developers to build immersive experiences and a dynamic virtual economy. Our Economy ML team sits at the heart of this mission, delivering scalable machine learning systems that power personalization, pricing, search, and content understanding across all Economy surfaces: Marketplace, Developer Monetization, Payments, and Avatar.We’re looking for a Distinguished Engineer/Technical Director to lead the strategy and technical direction for ML systems, with a focus on large-scale recommendations, infrastructure, and emerging Generative AI applications. You’ll help build the systems that support retrieval, ranking, generative modeling, and LLM-powered personalization, all at massive scale. This role requires deep systems thinking, hands-on ML expertise, and a vision for how traditional ML and GenAI come together to power the future of the Roblox economy.Why Roblox for ML SystemsAI/ML is a top company priority, with long-term investment.Real-world scale: Power millions of daily economic interactions across ranking, pricing, fraud, and search.Full-system ownership: Build and optimize end-to-end ML pipelines; from data ingestion to GPU training to live inference.Complex surfaces, rich signals: Design systems for 3D content, UGC, avatar metadata, behavioral and economic signals, not just text or images.Impact meets innovation: Shape the future of the Roblox economy while working on frontier ML infra and GenAI workflows.You will:Set the technical direction for ML systems powering core economy experiences: personalization, ranking, generative modeling, and moreBuild and evolve infrastructure for training, serving, and evaluating both traditional ML models and Generative AI models (e.g., embedding-based retrieval, transformers, LLM-based workflows)Partner with GenAI teams to explore multi-modal embeddings, avatar generation, and retrieval-augmented generation (RAG) in economic surfacesLead efforts to optimize ML system performance: low-latency serving, efficient GPU training, and cost-aware inference strategiesGuide the development of robust ML pipelines, online feature stores, and experimentation platforms that support both predictive and generative use casesCollaborate with EMs, tech leads, and product partners to align technical architecture with business prioritiesMentor senior engineers and help establish a strong technical culture across the Economy ML teamEnsure robustness, observability, and scalability of ML systems that power millions of daily economic interactionsOwn the architecture behind some of Roblox’s most foundational ML systemsWork across groups at Roblox to advance the state of ML/AI at the company.Help scale a fast-growing ML organization and define Roblox’s future economy intelligence stackSolve real-world problems with real user impact, powering millions of personalized, intelligent economic interactions every dayYou have:10+ years of experience in software engineering or ML infrastructure, with a strong focus on large-scale ML systemsDeep expertise in building recommendation systems, ranking infrastructure, or real-time personalization enginesHands-on experience with Generative AI, such as transformer-based models, LLMs, embeddings, or RAG pipelinesProven experience with distributed training, model deployment, and GPU-accelerated workflowsStrong understanding of ML system architecture: data pipelines, feature stores, inference optimization, experimentation toolingSystems-first mindset with strong instincts around scale, cost, performance, and reliabilityTrack record of leading large technical initiatives and mentoring senior engineersBS, MS, or PhD in Computer Science, Machine Learning, or a related fieldFor 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$397,460—$455,720 USDRoles 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 also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.