Senior Machine Learning Engineer II, Ads Response Prediction

🕒 May 28

🏄 California, Colorado, +17 more states – Remote

🏄 California – Remote ⛰️ Colorado – Remote 🦌 Connecticut – Remote 🏛️ District of Columbia – Remote 🌺 Hawaii – Remote 🌽 Illinois – Remote 🦞 Maine – Remote 🗻 New Hampshire – Remote 🏖️ New Jersey – Remote 🗽 New York – Remote 🌲 Oregon – Remote 🦀 Maryland – Remote 🍂 Massachusetts – Remote 🔔 Pennsylvania – Remote ⚓ Rhode Island – Remote 🤠 Texas – Remote 🍁 Vermont – Remote ⚔️ Virginia – Remote ☕ Washington – Remote

💵 $201k - $253.5k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Instacart

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2012

🛍️ eCommerce

🚗 Transport

🛒 Retail

💰 $232M Venture Round on 2021-11

eCommerce • Transport • Retail

Instacart is a company that offers a flexible approach to work while transforming the grocery industry. It provides an essential service by delivering groceries and household goods to customers' doors in as little as 30 minutes. Instacart offers safe and flexible earning opportunities to personal shoppers and tackles challenges such as rerouting deliveries during snowstorms and connecting customers with coupons and deals. It aims to be the operating system for the grocery industry, thus helping customers save time for other activities. Instacart emphasizes diversity, equity, and belonging in its work culture.

📋 Description

• Lead 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 adaptors for scalable domain fine-tuning. • Drive 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 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.

🎯 Requirements

• PhD/Master 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. • 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.

🏖️ Benefits

• Highly market-competitive compensation • Eligible for a new hire equity grant • Annual refresh grants