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Build the simulation and AI systems that model tvScientific's CTV ad auctions before bids ever touch live traffic

Pinterest's tvScientific team is hiring a Software Engineer II to build simulation environments modeling CTV auction mechanics, bidding strategy, and campaign outcomes. You'll design counterfactual frameworks and AI agents that de-risk ML model deployments before they hit production. This role sits at the intersection of systems programming, probabilistic modeling, and adtech.

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What this interview tests

Simulation environment design for auction/marketplace systems
Systems programming (Zig/C/C++/Rust) tradeoffs
Probabilistic modeling, stochastic processes, agent-based simulation
RTB/programmatic advertising mechanics
AI-assisted engineering with critical verification
Translating business questions into simulation frameworks

Common question themes

Design a simulation of a CTV ad auction with multiple bidders and inventory constraints

Why choose a systems language like Zig/Rust over Python for this kind of engine

How would you build a counterfactual framework to evaluate a new bidding strategy offline

Describe a time you used an AI agent to explore a strategy space or automate an experiment

How do you validate a new ML model in simulation before it touches live traffic

How do you verify AI-assisted code/data work before shipping it

How candidates describe it

Real Software Engineer II interview stories — retold from candidates' public write-ups, with sources.

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