Principal Machine Learning Engineer, Communication Safety
RobloxSan Mateo, CA, United StatesPosted 17 March 2026
Job Description
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 Safety?
At Roblox, we strive to connect a billion people with optimism and civility, and the Safety organization’s mission is to become the leader in civil immersive online communities. We systematically and proactively detect, remove, and prevent problematic content and behavior. In doing so, we keep Roblox safe, civil, and inclusive, fostering positive relationships between people around the world.
Why Safety AI Systems?
As a Senior/Principal Machine Learning Engineer for Safety AI Systems, you will define the future of proactive moderation, driving immense social impact through cutting-edge, innovative ML solutions. This role is a fusion of a Principal Engineer's technical authority and an Engineering Manager's leadership, focused on the most critical and ambiguous safety challenges.
You will set the 3-5 year technical strategy and architectural blueprint for how Roblox uses machine learning—from high-velocity text filtering to leveraging multimodal Generative AI—to combat the most complex critical harms on the platform. You will own the execution roadmap and technical planning, directly guiding the launch of high-priority new ML projects. You will be responsible for defining the standards of innovation in data quality, model robustness, and ethical deployment across the entire Content Communication Safety ML pillar.
You will lead and scale a high-performing group of individual contributors, providing both strategic oversight and hands-on guidance to the teams responsible for the architectural development of massive-scale systems that mitigate abuse and harassment before it impacts our community.
You will feel a deep sense of responsibility in proactively protecting our community—thoughtfully and fairly—while balancing user freedom with platform civility. Your efforts will ensure Roblox remains one of the safest places on the internet for users.
You will:
Define and Own the Technical Vision: Define and lead the multi-year technical vision, architectural strategy, and execution for machine learning solutions across Content and Communication Safety, ensuring these systems proactively and effectively detect and prevent high-severity, critical harms at massive scale.
Provide Senior/Principal-Level Architecture: Act as the highest technical authority for the Content Safety ML domain, guiding the architecture and long-term maintainability of foundational models, data pipelines, and real-time inference services.
Drive Cross-Org Alignment: Identify and champion the most ambiguous, high-leverage technical problems, driving alignment and securing investment for organization-wide ML infrastructure and platform development initiatives that benefit all of Trust Safety.
Lead Innovation in Safety: Oversee the adoption and safe deployment of innovative technologies (e.g., advanced NLP, self-supervised learning, multimodal LLMs) to anticipate and mitigate novel abuse vectors, moving beyond reactive detection to proactive intervention .
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 ... (truncated, view full listing at source)
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