The opportunity
The Advertiser Optimization team is the decision-making engine of Instacart's $1B+ ads business. We own the systems responsible for Bidding, Pacing, Budgeting, and Targeting: converting stated advertiser goals into real-time auction actions.
What you'll do
Design and evolve real-time bid optimization systems that translate: advertiser goals (target ROAS, budget constraints) into optimal auction bids under uncertainty. Formulate the bidding problem as constrained optimization and build the feedback mechanisms that keep bids aligned with realized outcomes.
Build intelligent budget pacing algorithms that distribute spend across time: and auction opportunities. The core challenge: allocating a finite daily budget across stochastic demand while maximizing total value, subject to advertiser constraints and time-varying conversion dynamics.
Develop the analytical frameworks that connect bidding, pacing, and budgeting: into a coherent optimization objective.
Shape auction mechanics including reserve pricing, multi-slot allocation, and: bid-to-price mapping. Reason about mechanism design tradeoffs between advertiser outcomes, platform revenue, and marketplace efficiency.
Own the full research-to-production loop: diagnose system behavior from large-scale data, formulate hypotheses, design experiments, ship production code, and measure impact. Write technical strategy documents that set the algorithmic direction for the team.
Graduate degree (Masters or PhD) in operations research, applied mathematics,: control systems, computational economics, or a related quantitative field.
What they're looking for
- Experience with real-time bidding systems, ad auction optimization, or computational advertising at scale.
- Background in budget-constrained allocation methods. Experience with adaptive: control or model-predictive control in production systems.
- Familiarity with causal inference and experimental design for evaluating: algorithmic changes in marketplace settings.
- Track record of shaping technical strategy and driving cross-functional: alignment between engineering, product, and data science.
