Netflix Ads Decisioning & Optimization engineer — real-time ad ranking, bidding, and pacing infra with sub-20ms ML serving

A senior (7+ years) distributed systems role on Netflix Ads' Decisioning & Optimization team, building the real-time ad decisioning path — ranking, scoring, bidding, and pacing — plus ML model serving infrastructure supporting dozens of concurrent hot-path models at sub-20ms P99 inference. Requires genuine ad-tech domain experience (2+ years) alongside distributed systems depth.

Practice this interview

Free · a live voice mock calibrated to this exact role

Start the mock interview

What this interview tests

Real-time ad decisioning: ranking, scoring, bidding, pacing under strict latency/throughput SLAs
ML model serving infrastructure at sub-20ms P99 (routing, fallback, calibration, lifecycle)
Ad-tech domain fundamentals: inventory management, frequency capping, supply-demand
Auction mechanics and budget pacing/delivery optimization
Simulation/offline validation frameworks for marketplace changes
Operational excellence: reliability, observability, incident response

Common question themes

Design a real-time ad ranking/bidding system meeting strict P99 latency budgets

How would you build ML model serving infra supporting dozens of concurrent hot-path models with fallback tiers

Explain auction mechanics you've implemented or reasoned about: first-price vs second-price, reserve pricing, bid shading

How would you design a budget pacing system to keep campaign delivery accurate across a campaign's lifetime

Describe productionizing a data science model into a low-latency serving path

How would you build a simulation framework to validate marketplace changes before live rollout

How candidates describe it

Real Distributed Systems Engineer 5 - Decisioning & Optimization interview stories — retold from candidates' public write-ups, with sources.

Google · L3 Software EngineerOffer

Google L3 software engineer interview: phone screen, four coding rounds, and the Googleyness round

A candidate with two years of experience went from recruiter outreach to offer over about four months. The onsite was four 45-minute coding rounds — three of them featuring binary trees — and one round turned into a 25-minute chain of follow-ups about approximating an optimal solution at scale.

Interviewed June 2020 · Bangalore, IN

Google · L4 Software EngineerNo offer

Google L4 Software Engineer Interview: Eight Rounds, No Offer

An L4 Software Engineer candidate went through two phone screens, three onsite rounds, a culture conversation, and a team-matching call with a Google hiring manager, then watched the process stall for about a month and a half over a tightened experience requirement before an added extended round ended without an offer.

Interviewed February 2024 · Not specified

Google · L5 Software EngineerNo offer

Google L5 software engineer interview: phone screening, three onsite rounds, system design, and a late rejection

A candidate interviewing for an L5 role went through a phone screening, three onsite coding rounds, a mobile system design round, and a Googleyness and Leadership round. Two of the four technical rounds went poorly by the candidate's own assessment, and after roughly two months of silence the recruiter reported that the role had been closed.

Interviewed January 2023 · Not specified

View the original posting

All Netflix Distributed Systems Engineer interviews

All Netflix interviews