Senior Machine Learning Engineer II, Ads Response Prediction

ABOUT THE ROLE As a Senior Machine Learning Engineer II, Ads Response Prediction, you will lead the design and development of core machine learning 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. WHAT YOU'LL DO You will tackle fundamental 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 also have the opportunity to shape our next-generation foundation model approach for ads ranking and contribute to 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 machine learning 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. Your responsibilities will include: - Leading research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases, and advancing model accuracy across Instacart's ads surfaces. - Designing and implementing debiasing techniques such as Mixed Negative Sampling, Inverse Propensity Weighting, counterfactual risk minimization, and calibration methods to address systematic prediction biases. - Contributing to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts, Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning. - Driving 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. - Collaborating with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction. - Formulating and scoping ambiguous modeling problems from first principles. Translating business observations into well-defined ML research directions with clear evaluation criteria. - Publishing and presenting findings internally. Contributing to the team's culture of technical rigor through design reviews, paper sharing, and experiment retrospectives. WHAT YOU'LL NEED To be successful in this role, you will need: - A PhD or Master's degree in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field. - 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale. - A deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep & Wide, DeepFM, DCN, and multi-task learning formulations. - A 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 (