Principal Machine Learning Engineer, Alt Defense at Roblox

Roblox

San Mateo, CA, United States·1w ago

LeadFull-timeReactAWSMachine LearningGitAIML

Salary Range

$153,000 - $207,000 /year

Estimated

This salary is estimated based on similar roles. The actual salary may vary.

About the Role

We are looking for a Technical Lead to join our Alt Defense pod at Roblox. This role focuses on building an industry-leading detection system for alternate account detection and prevention, operating at a massive scale to keep Roblox safe and civil.

Key Responsibilities

→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.

Requirements

✓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.