Mid
Build the feature and training-data infrastructure that powers Reddit's Ads ML systems
Reddit's Ads Engineering org is hiring a Machine Learning Systems Engineer to build and scale the feature management platform behind Ads ML — batch and real-time feature pipelines, training set generation, and feature governance. This is an infrastructure-and-platform role, not a modeling role: strong distributed-systems chops and production data-pipeline experience matter more than model tuning.
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What this interview tests
- Large-scale feature/training-set data infrastructure design
- Distributed systems (Spark/Flink/Kafka/Ray/Airflow/Kubernetes/BigQuery)
- Feature governance: lineage, drift detection, versioning, reproducibility
- Agentic/automated ML workflow tooling
- Partnering with ML engineers on production integration
- Operational excellence: observability, reliability, cost optimization
Common question themes
Design a batch or real-time feature computation pipeline at scale
Debug a production data pipeline failure and walk through root cause
How would you detect and handle feature drift or data anomalies in production
Tradeoffs between build vs. buy for a distributed compute system
Designing for reproducibility and versioning in a feature store
How would you support an agentic workflow for automated feature discovery