Senior Machine Learning Engineer, Ads Response Prediction

We're transforming the grocery industry

At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.

Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.

Instacart is a Flex First team

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Overview

As a Senior Machine Learning Engineer on the Ads Response Prediction team, you will own and execute the development of ML models that power Instacart's ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will work on meaningful challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer's curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will contribute to our foundation model approach for ads ranking and work on cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models.

The Ads Response Prediction team owns all systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, sequential modeling and generative retrieval systems for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue.

The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure.

About the Job

Own and execute research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer's curse), and advancing model accuracy across Instacart's ads surfaces.

Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases.

Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adapters for scalable domain fine-tuning.

Contribute to sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements.

Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction.

Formulate and scope ambiguous modeling problems within your project scope from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.

Publish and present findings internally. Contribute to the team's culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.

About You

Minimum Qualifications

Master's or PhD in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field; or equivalent experience.

3+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale.

Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning formulations.

Strong foundation in causal inference, counterfactual reasoning, and training data bias mitigation. Ability to reason about selection bias, position bias, and propensity-based correction methods.

Proficiency in Python and deep learning frameworks (PyTorch, Tensorflow, JAX). Fluency in data manipulation tools (SQL, Spark, Pandas).