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

How candidates describe it

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

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