Senior
Build the ML systems behind real-time CTV ad bidding at Pinterest's tvScientific
Own the machine learning powering tvScientific's Connected TV ad-buying platform — real-time bidding, campaign optimization, and incrementality measurement across Hulu, Pluto TV, Disney+, HBO Max, and hundreds of FAST channels. This is production ML in adtech, not research: you'll write prod Python that makes millions of bid decisions per second and prove advertisers' CTV spend actually drove business outcomes.
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Free · a live voice mock calibrated to this exact role
What this interview tests
- Real-time bidding system design at scale
- Production ML deployment and monitoring
- Causal inference / incrementality measurement
- Statistics and experiment design fundamentals
- Adtech/CTV/RTB domain knowledge
- Technical leadership and mentoring on a distributed team
Common question themes
Design a real-time ad-bidding pipeline handling millions of decisions/sec
How would you measure incrementality/causal lift of a CTV campaign
When do you pick a simpler model over a complex one, and how do you evaluate that tradeoff
How do you monitor a production pricing/bidding model for drift
Describe using AI coding assistants or LLM tools in your actual dev workflow
How do you mentor engineers and communicate decisions on a distributed team
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