Senior

Build Reddit's next-gen ML experimentation and agentic AI platform for Ads ML

Design and build large-scale offline ML experimentation platforms, production training orchestration frameworks, and an agentic AI execution platform that powers Reddit's Ads ML lifecycle from experimentation through autonomous operations. A fit for a senior infra/platform engineer with 5+ years in distributed systems and 2+ years building production ML infrastructure who wants to define foundational tooling for multi-agent, human-in-the-loop workflows.

Practice this interview

Free · a live voice mock calibrated to this exact role

Start the mock interview

What this interview tests

Offline ML experimentation platform design (reproducibility, promotion workflows)
Distributed training orchestration (hyperparameter tuning, automated retraining)
Distributed data processing systems (Spark/Flink/Ray) and workflow orchestrators (Kubeflow/Argo/Airflow)
Experiment tracking, lineage, model registries, artifact versioning
Agentic AI execution platforms (multi-agent orchestration, MCP/A2A, memory/context systems)
Partnering with ML engineers/researchers to raise experimentation velocity

Common question themes

Design an offline ML experimentation platform that guarantees reproducibility at scale

How would you build a training orchestration framework supporting distributed training and automated retraining

Compare your experience with Spark/Flink/Ray or Kubeflow/Argo/Airflow — what tradeoffs drove your choice

Design a model registry with lineage, versioning, and rollback support

How would you architect a multi-agent orchestration system with memory/context management

Tell me about a production ML infrastructure incident and how you diagnosed it

How candidates describe it

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

Google · L5 Machine Learning EngineerOffer

Google L5 machine learning engineer interview: phone screen skipped, four technical rounds, and an offer

A candidate applying for an L5 machine learning engineer role at Google had the phone screen skipped due to a referral and prior tenure at the company, then went through four technical and design rounds plus a behavioral round before receiving an L5 offer. The loop was one leg of a broader search that produced offers from several companies in the same cycle.

Interviewed April 2022 · Remote

Amazon · Applied Scientist (L4)Offer

Amazon Applied Scientist Interview Experience: Alexa Speech Team, 2021

A redirected recruiter call turned into an Amazon Applied Scientist loop with the Alexa Speech team: a phone screen, a split five-round virtual onsite across two teams, a bar raiser, and an added ML-breadth round, ending in an offer with a downlevel from L5 to L4.

Interviewed July 2021 · Boston, MA (Remote)

View the original posting

All Reddit interviews