Own the ML roadmap that catches Roblox outages before 100M daily users feel them

Roblox is hiring its first Senior ML Engineer inside the Reliability team, tasked with using anomaly detection and time-series modeling across logs, traces, and metrics to cut Mean Time to Detect and Mean Time to Resolve production incidents. This interview probes applied ML for systems reliability, distributed-systems fundamentals, and the judgment to set technical direction in an ambiguous, first-of-its-kind role.

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What this interview tests

Real-time anomaly detection on production telemetry (metrics/logs/traces)
Time-series forecasting for capacity and traffic-spike prediction
Root-cause reasoning systems that fuse multiple data streams
Distributed systems fundamentals at high-throughput scale
Setting technical direction/roadmap in a newly-formed function

Common question themes

Design a pipeline to detect anomalies across logs, metrics, and traces in near-real-time

How would you reduce MTTD/MTTR using ML rather than static thresholds

Build a time-series model to predict capacity exhaustion before it happens

How do you avoid alert fatigue / false positives in an automated detection system

Tell me about a time you set technical direction with no existing playbook

How would you explain a complex ML reliability system to non-technical executives

How candidates describe it

Real Senior Machine Learning Engineer interview stories — retold from candidates' public write-ups, with sources.

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