Why User Behavior?

As a Senior Machine Learning Engineer for User Behavior, you will build the proactive detection and behavioral intelligence systems that fundamentally shift how Roblox responds to unsafe behavior, from reactive punishment to early, education-first intervention.

You will own the ML foundation for a new capability at Roblox: identifying users at risk of becoming chronic violators before they escalate, and powering the adaptive consequence systems that give them a real chance to change. This is a rare opportunity to build a user-level behavioral intelligence capability that will define how Roblox understands and responds to risk at scale.

You will feel a deep sense of responsibility in building systems that directly affect millions of users, especially minors, on one of the world's largest platforms. Your work will reduce harm, improve fairness, and help ensure Roblox remains a place where creativity and community can thrive safely.

You will:

• Define and Own the ML Vision: Define and lead the multi-year ML vision, architectural strategy, and execution that identify casual violators early before they escalate, and operate reliably under real-world policy and precision constraints.
• Own Behavioral Prediction Models: Develop and maintain production ML models that translate user behavioral signals into actionable risk intelligence, enabling earlier and more targeted interventions.
• Build the ML Foundation from Scratch: Design the user feature store, training pipelines, and model evaluation infrastructure that underpin behavioral ML at Roblox.
• Instrument Experiments for Causal Validity: Partner with XFN stakeholders to ensure A/B tests on intervention variants have proper random assignment and instrumentation for future causal model training. This data cannot be retroactively collected.
• 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 labeling pipelines, and ship solutions to various product surfaces.
• Ship Code, Not Just Models: Expect to own end-to-end delivery, building and operating ML pipelines from training and offline evaluation through production serving. You will integrate your models directly into the stack alongside SWE.

You have:

• 5+ years of experience building and shipping production ML systems at scale.
• Hands-on experience with ML classification at scale, including traditional ML models, sequence modeling on user-event data, and/or LLM-based approaches, with strong intuition for model evaluation on real-world data.
• Proficiency designing and maintaining feature pipelines and feature stores for behavioral or event-driven data.
• Experience building embedding-based retrieval systems (ANN / vector search) in production.
• Experience building and operating end-to-end ML pipelines: training, offline evaluation, A/B experiment hooks, and production serving infrastructure.
• Strong understanding of ML experiment design, including training data validity, label quality, recall/precision tradeoffs under constrained thresholds, and causal inference fundamentals.
• Nice to have: experience in Trust & Safety, user behavior modeling, or user understanding (e.g., churn prediction, engagement modeling, risk scoring); familiarity with uplift/causal ML; sequence models on user-event data.

You are:

• A Visionary Architect: Capable of synthesizing complex business and safety goals into a clear, compelling, and actionable technical strategy.
• A Pragmatic Builder: You are scrappy and impact-oriented. You view undefined data and messy systems as opportunities to build structure rather than blockers to progress.
• Comfortable with Ambiguity: You thrive in undefined or open-ended problem spaces, providing structure, clarity, and decisive direction to your teams.
• An Inspiring Leader: Passionate about developing the next generation of technical leaders, managers, and engineers.
• An Executive Communicator: Highly effective at communicating complex technical concepts to both engineering teams and non-technical executive leadership.
• Committed to Ethical AI: Dedicated to building ML systems that are fair, transparent, and operate with the utmost responsibility toward user safety and platform civility.