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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
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