Principal Machine Learning Engineer - Reliability

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. Why Reliability? Roblox serves over 100 million people every day across a platform that is constantly evolving — and behind every experience is infrastructure that has to work, every time, at massive scale. The Reliability team at Roblox operates at the depth and breadth of the Roblox stack. Availability of the platform is a key company goal. We are hiring our first Principal Machine Learning engineer within our team. As a Principal Machine Learning Engineer within Reliability, you will set the 3-5 year technical strategy and architectural blueprint for how machine learning systems/practices can be leveraged to improve the reliability of the overall Roblox platform. You will own the architectural and execution roadmap of leveraging massive data across - logs, traces, metrics, production changes, to proactively detect issues before they become real problems (MTTD) and/or reduce time to resolve incidents (MTTR). You will have the opportunity to cross functionally collaborate with other similar teams at Roblox to define best practices and software. You will: Define and Own the Technical Vision: Define and lead the multi-year technical vision, architectural strategy, and execution for machine learning solutions in Content Safety, ensuring these systems proactively and effectively detect and mitigate violative content at massive scale. Strategic Stakeholder Partnership: Collaborate with executive-level Product, Data Science, Policy, and Operations leaders to define and prioritize the strategic machine learning roadmap, influencing product strategy and demonstrating the impact of ML on user trust and safety outcomes. Lead Innovation: Oversee the adoption and safe deployment of innovative machine learning techniques (e.g., transfer-learning, self-supervised learning, quantization, LoRA, distillation). Drive End-to-End Product Development: You will not just model; you will build. You will work cross-functionally to construct datasets from scratch where none exist, build auto-labeling pipelines, and ship solutions to solve novel technical problems. Ship Code, Not Just Models: Expect to spend roughly 30-40% of your time on backend and integration work . You will be responsible for integrating your work into the production stack, leveraging modern AI coding tools (e.g., Cursor) to accelerate velocity and handle infrastructure complexity You have: 8+ years of experience designing, developing, and operating large-scale, high-impact machine learning systems in a production environment. A proven track record of successfully setting the long-term technical direction for an entire ML domain, demonstrating the ability to take ambiguous problems from concept to scaled production impact. Deep expertise in advanced ML architectures and techniques, including Computer Vision (CV) and/or Vision-Language Models (VLMs) Expertise in architecting scalable, real-time ML inference services and robust data pipelines Demonstrated success in leading and resolving high-stakes, cross-functional conflicts and technical disagreements, with an ability to build consensus among diverse stakeholders. Exceptional product sense and strategic planning ability: able to t ... (truncated, view full listing at source)