Principal Machine Learning Engineer, Alt Defense

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 detect, remove, and prevent problematic accounts, content and behavior, and we make Roblox accounts secure and free from compromise. We cover a broad area of the tech spectrum, including machine learning, classifiers for 3D models, experimentation, automation, detection workflows, and AI-powered text filters. Aligned and partnering with product teams, we use this toolbelt to discover new opportunities, influence and shape the product roadmap and prioritization, build safety products, and measure the impact on our community of users and developers. In doing so, we keep Roblox safe, civil, and inclusive, and we foster positive relationships between people around the world.

WHY ALT DEFENSE?

Safety and Civility is Roblox’s #1 priority. The Alt Defense pod is at the front lines of this mission, tasked with solving one of the most difficult adversarial problems on the platform: Alternate Account Detection and Prevention. When bad actors are removed from Roblox, they often attempt to return immediately with new identities. Our goal is to stop them in their tracks. As the Technical Lead for this pod, you will architect and build an industry-leading detection system that operates at a massive scale—processing billions of accounts and identifying recidivism within minutes. You will also be supporting additional use cases for alt detection across the company. You will report to the Senior Engineering Manager of the Account Identity Team.

You will:

• Lead the technical vision for alternate account detection platform, moving from reactive measures to proactive, near real-time prevention.


• Architect high-scale ML systems using Graph Neural Networks (GNNs) and advanced clustering techniques to map relationships across billions of entities.


• Solve complex ground truth and training data challenges for adversarial usecases  


• Build for latency and scale, ensuring that detection happens within minutes of a bad actor’s attempt to rejoin the platform.


• Develop innovative adversarial approaches to stay ahead of sophisticated actors who use evolving techniques to mask their identity.


• Drive the ML roadmap, identifying opportunities to leverage big data and behavioral signals to improve precision and recall in a high-stakes environment.


• Mentor and up-level a pod of high-performing ML and software engineers, fostering a culture of technical excellence and rapid iteration.

You have:

• MS or PhD degree in Computer Science, Machine Learning, or a related field.


• 10+ years of industry experience in Applied ML, with a significant focus on anti-abuse, fraud, integrity, or identity.


• Expertise in Graph Learning: Deep experience with Large-scale GNNs (GraphSAGE, PGB, etc.) and unsupervised/semi-supervised clustering at the scale of billions of nodes.


• Proven track record of leading complex technical projects from conception to production-level deployment.


• Experience with high-throughput systems: You understand the nuances of deploying ML models in low-latency environments where "time-to-detect" is a critical KPI.


• Adversarial mindset: You can think like a bad actor to anticipate how they will circumvent detection and build robust defenses against it.

You are: 

• Resourceful: You're adaptable in any situation and can always find a path forward in the face of evolving threats.


• Analytical: Excited to investigate large, ambiguous datasets to find the "signal in the noise" of billions of accounts.


• User Oriented: You understand that safety measures must be balanced with a seamless experience for our legitimate community members.


• Team Oriented: You are a sought-after mentor who lifts up your peers and builds a collaborative, high-performance environment.


• Mission Oriented: You are laser-focused on keeping Roblox safe and are willing to take bold, strategic risks to achieve a billion-user scale safely.