Applied scientist setting the algorithmic direction for real-time bidding, pacing, and budgeting in a $1B+ ads business

Instacart's Advertiser Optimization team is hiring a senior applied scientist to own the mathematical and production direction of bidding, pacing, budgeting, and targeting systems that make millions of real-time auction decisions per day. The role requires deep grounding in control theory, constrained/stochastic optimization, and auction/mechanism design, translated into low-latency production code.

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What this interview tests

Constrained/stochastic optimization for real-time bid and pacing decisions
Feedback control theory (PID, MPC) applied to budget pacing under stochastic demand
Auction theory and mechanism design (reserve pricing, multi-slot allocation, bid-to-price mapping)
Production systems engineering under strict latency (sub-100ms, millions of decisions/day)
Research-to-production loop: hypothesis, experiment design, shipped code, impact measurement
Causal inference / experimental design in marketplace settings with interference

Common question themes

Formulate real-time bid optimization as a constrained optimization problem under uncertainty

Design a budget pacing algorithm that allocates finite daily spend across stochastic demand

Justify a control-theory or optimization technique choice (e.g., MPC vs. simple proportional control) for a pacing system

Explain how auction mechanics (reserve price, multi-slot allocation) affect advertiser and platform outcomes

Walk through taking a mathematical formulation into low-latency production code

Design an experiment to evaluate an algorithmic change in a marketplace where standard A/B testing has interference

How candidates describe it

Real Senior Applied Scientist II interview stories — retold from candidates' public write-ups, with sources.

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