Instacart

Senior Applied Scientist II, Ads Optimization

Instacart$240K — $253K *
US-AnywhereRemote in United States
Enterprise Technology
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • MS or PhD in operations research, applied mathematics, or related field.
  • 8+ years in building and deploying optimization systems in production.
  • Strong foundation in control theory, optimization, and auction design.
  • Proficiency in Go, Java, C++, and Python.
  • Experience translating mathematical models into scalable production code.

Responsibilities

  • Design real-time bid optimization systems based on advertiser goals.
  • Build algorithms for intelligent budget allocation across time and auctions.
  • Develop frameworks connecting bidding, pacing, and budgeting.
  • Shape auction mechanics including pricing and allocation strategies.
  • Own the research-to-production loop from diagnostics to impact measurement.

Benefits

  • Remote work flexibility under the Flex First policy.
  • Participation in equity grants for new hires and annual refreshes.
Full Job Description
Overview

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. Our mission is to maximize realized Advertiser Value by deciding when to participate, how much to bid, and how fast to spend, all while balancing User Experience and Platform Revenue.

We are hiring a Senior Applied Scientist II to lead the algorithmic direction of these systems. This is a role for someone who thinks in terms of control theory, constrained optimization, and auction economics, and who can translate those frameworks into production code that makes millions of decisions per day. You will formulate problems from first principles, shape the technical roadmap, and own systems end-to-end from mathematical design through production deployment through impact measurement.
About the Job
  • 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.
About You
Minimum Qualifications
  • MS 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 (not just research prototypes).
  • Strong foundation in at least two of: feedback control theory (PID, MPC), convex and stochastic optimization, auction theory and mechanism design, dynamic programming.
  • Proficiency in one of the following languages: Go, Java, 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 (millions of decisions per day, sub-100ms latency constraints).
Preferred Qualifications
  • 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.


Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.

Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.

For US based candidates, the base pay ranges for a successful candidate are listed below.

CA, NY, CT, NJ

$240,000-$253,500 USD

WA

$230,000-$243,000 USD

OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI

$221,000-$233,000 USD

All other states

$201,000-$212,000 USD

About Instacart

Instacart is an American company that operates a grocery delivery and pick-up service in the United States and Canada. The company offers its services via a website and mobile app. The service allows customers to order groceries from participating retailers with the shopping being done by a personal shopper. Instacart was founded in 2012 by entrepreneur Apoorva Mehta, a former Amazon.com employee. Apoorva was born in India and moved with his family to Canada in 2000. He studied engineering at the University of Waterloo and graduated in 2008. He was a participant in Y Combinator's Summer 2012 batch, which eventually led to the creation of Instacart. In 2013, Mehta was included on the Forbes 30 Under 30 list. Apoorva previously worked at BlackBerry, Qualcomm, and then Amazon as a supply chain engineer, where he developed fulfillment systems to move packages from Amazon's warehouses to customers' homes. Before founding Instacart, Apoorva had tried to start at least 20 other services. He tried building an ad network for social gaming companies, and developing a social network specifically for lawyers, among other start-ups. Instacart originally launched in San Francisco. By April 2015, the firm had about 200 employees. It introduced a new policy around June allowing some shoppers to choose to be part-time employees, starting with Chicago and Boston and extending its offer to shoppers in Atlanta, Miami, and Washington D.C. the following month.
Learn more about Instacart
Industry
Founded
2012

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