This role pays more than
66%
of similar roles. Most pay
$191,197–$255,900
— the shaded band above.
At the midpoint, this role pays about
$247k
versus about
$224k
for comparable roles.
Based on 240 similar postings.
Employer
About Instacart
Instacart (Maplebear Inc.) is a North American grocery technology company that operates an online marketplace and delivery service connecting customers with personal shoppers who pick and deliver orders from local retailers. It also sells retail e-commerce software and advertising services to grocers and brands.
Instacart currently has
54 open roles
on FindRole.
Listed pay typically runs
$196,000–$207,000
across 54 roles with salary data.
Senior Applied Scientist II, Ads Optimization joins the Advertiser Optimization team to lead the algorithmic direction of systems governing bidding, pacing, budgeting, and targeting. This role involves formulating problems from first principles to translate advertiser goals into real-time auction actions while balancing user experience and platform revenue. The successful candidate will design bid optimization systems under uncertainty, build intelligent budget pacing algorithms for stochastic demand, and shape auction mechanics like reserve pricing and multi-slot allocation. Key responsibilities include owning the full research-to-production loop, from mathematical design to production deployment. Required expertise includes control theory, constrained optimization, and auction economics. Candidates must possess a graduate degree in a quantitative field and proficiency in Go, Java, or C++ for production systems, along with Python for data analysis. The role addresses complex problems in computational advertising and marketplace efficiency.
What does a Applied Scientist earn?
Median $231000 from 40 postings across 11 companies.
Design and evolve real-time bid optimization systems to translate advertiser goals into optimal auction bids.
Build intelligent budget pacing algorithms to distribute spend across stochastic demand while meeting advertiser constraints.
Develop analytical frameworks that integrate bidding, pacing, and budgeting into a single coherent optimization objective.
Shape auction mechanics including reserve pricing, multi-slot allocation, and bid-to-price mapping.
Translate complex mathematical formulations into production code capable of making millions of decisions per day at low latency.
Own the full research-to-production loop by diagnosing system behavior, formulating hypotheses, and designing experiments.
Write technical strategy documents to define the algorithmic direction for the Advertiser Optimization team.
What we're looking for
Graduate degree (Masters or PhD) in operations research, applied mathematics, control systems, computational economics, or a related quantitative field.
8+ years of experience building and deploying optimization or control systems in production environments.
Strong foundation in at least two of: feedback control theory, convex and stochastic optimization, auction theory and mechanism design, or dynamic programming.
Proficiency in Go, Java, or C++ for production systems and Python for data analysis and offline pipelines.
Demonstrated ability to translate mathematical formulations into production code that runs at scale with low latency constraints.
Experience with real-time bidding systems, ad auction optimization, or computational advertising at scale (preferred).
Background in budget-constrained allocation methods, adaptive control, or model-predictive control in production systems (preferred).
Familiarity with causal inference and experimental design for evaluating algorithmic changes in marketplace settings (preferred).
Machine Learning
Reinforcement Learning
Python
Java
Spark
Hadoop
Optimization Algorithms
Design of Experiments
Statistics
Control Theory
Forecasting
Agile
Production Code
Data Science
Machine Learning
Causal Inference
Statistical Modeling
Adaptive Control
model-predictive control
Production Systems
Analytics
Budget Allocation
Remote (Ontario, Canada)+38 days ago
$145,000–$153,000
Python
SQL
Machine Learning
Optimization
Statistical Modeling
Econometrics
Control Theory
Data Pipelines
Auction Theory
Algorithm Implementation
Probability
Mathematics
Statistics