Principal Machine Learning Engineer, Alt Defense

Roblox Roblox · Consumer · San Mateo, CA · Software Engineering

Roblox is seeking a Principal Machine Learning Engineer to lead the technical vision for their Alternate Account Detection platform. This role involves architecting and building high-scale ML systems, specifically using Graph Neural Networks and advanced clustering techniques, to detect and prevent bad actors from rejoining the platform in near real-time. The engineer will also drive the ML roadmap, mentor other engineers, and focus on adversarial approaches to stay ahead of sophisticated actors.

What you'd actually do

  1. Lead the technical vision for alternate account detection platform, moving from reactive measures to proactive, near real-time prevention.
  2. Architect high-scale ML systems using Graph Neural Networks (GNNs) and advanced clustering techniques to map relationships across billions of entities.
  3. Solve complex ground truth and training data challenges for adversarial usecases
  4. Build for latency and scale, ensuring that detection happens within minutes of a bad actor’s attempt to rejoin the platform.
  5. Develop innovative adversarial approaches to stay ahead of sophisticated actors who use evolving techniques to mask their identity.

Skills

Required

  • 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.

Nice to have

  • Mentoring and up-leveling engineers
  • Fostering a culture of technical excellence and rapid iteration

What the JD emphasized

  • 10+ years of industry experience in Applied ML
  • Expertise in Graph Learning: Deep experience with Large-scale GNNs
  • Experience with high-throughput systems
  • time-to-detect is a critical KPI

Other signals

  • Graph Neural Networks
  • large-scale ML systems
  • adversarial use cases
  • near real-time prevention
  • billions of accounts