Senior Machine Learning Engineer, Ads Response Prediction
About the role
Senior ML Engineer on the Ads Response Prediction team at Instacart, focusing on research-led ML modeling for pCTR/conversion prediction, debiasing, multi-domain multi-task architectures, and retrieval systems, with an emphasis on modeling science over infrastructure.
Responsibilities
- 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.
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 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).
- Track record of formulating ambiguous problems into well-scoped ML research directions and delivering results through rigorous experimentation.
- Strong written and verbal communication skills. Ability to explain complex modeling decisions to cross-functional stakeholders including product managers and data scientists.
Skills
About Instacart
Marketplace for grocery delivery and pickup
Instacart is the North American leader in online grocery and one of the fastest-growing companies in e-commerce. Instacart’s same-day delivery and pickup services bring fresh groceries and everyday essentials to busy people and families across the U.S. and Canada in as fast as an hour. Since its founding in 2012, Instacart has become an essential service for millions of families, while also serving as an immediate, flexible earnings opportunity for hundreds of thousands of shoppers across North America. The company partners with more than 350 retailers and delivers from more than 25,000 stores across more than 5,500 cities in North America. Today, Instacart is accessible to more than 85% of households in the U.S. and more than 70% of households in Canada.
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