Machine Learning Engineer, PhD Intern (Fall)

Machine Learning PhD Intern, Economics (Fall)

Instacart

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 There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview We are looking for interns to join Instacart’s Economics team. The ideal candidate for this role will bring a combination of experience in both economics and machine learning. We are in particular looking for current or recently graduated PhD students in economics or related fields like marketing, finance, or operations research. Candidates should bring some relevant research experience, typically in computationally intensive empirical topics, as well as some exposure to machine learning coursework and applications. The Economics team at Instacart works on a range of interesting and challenging problems at the intersection of machine learning and economics, from aligning the incentives in our multi-sided marketplace to analyzing the impact of behavioral nudges on our customers’ and shoppers’ decisions. Some of the core areas of focus for our team include online advertising, uplift and long term value modeling, logistics, marketplace optimization (consumers, shoppers, retailers), inventory intelligence, and general causal inference. You can find more information in our blog post that introduces the team and the type of work we do. About the Job * You will help design and build end-to-end machine learning solutions. * You will be working in small and cross-functional product teams, with great opportunities for growth and ownership of projects. * You will be an active member of an internal community, including economists, data scientists, operations research scientists and machine learning engineers, sharing learnings, best practices and research across many domains. * You will develop high impact solutions to support Instacart's ambitious growth plans. * You will work closely with engineers, product managers, other teams, and both internal and external stakeholders, owning a large part of the process from problem understanding to recommending a solution and testing it in controlled experiments. * You will have the freedom to suggest and drive organization-wide initiatives. About You Minimum Qualifications * Current or recently graduated PhD student in economics or a related field with focus on data-intense problems. * A blend of economic theory, applied econometrics, and business acumen that let you jump into a fast-paced environment and contribute from day one. * Expertise in causal inference with observational and experimental data. * Expertise in Python or R and fluency in data manipulation (SQL, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools. * Self-motivation and a strong sense of ownership 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. 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 $50—$50 USD WA $48—$48 USD OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $44—$44 USD All other states $42—$42 USD

Senior Machine Learning Engineer, Operations Research

Instacart

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 There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. Overview: We are looking for a Senior Machine Learning Engineer with a strong Operations Research background to join the Service Availability & Routing team within Instacart's Logistics organization. In this role, you will work at the intersection of combinatorial optimization, mathematical programming, and AI to solve high-impact problems in the fulfillment space — including order batching, shopper routing, service availability prediction, and real-time assignment. You'll partner closely with engineering, product, and data science to ship models and algorithms that directly influence Instacart's profitability and shopper experience at scale. The Logistics & ML group is responsible for the intelligence and execution behind Instacart’s fulfillment system. The team optimizes a multi-sided marketplace to ensure customers get their orders on-time and in high quality, shoppers get efficient and fulfilling work, and retailers and consumer brands get reasonable business. The team tackles hard problems in a variety of spaces, such as matching, pricing, and geospatial, as well as foundational problems executing on a high throughput system with dynamic data.   About the Job: * Design, develop, and deploy machine learning solutions to tackle practical challenges in the marketplace. * Collaborate closely with product managers, data scientists, and backend engineers to deeply understand business needs and create impactful ML applications. * Actively engage with diverse stakeholders to ensure that solutions are well-integrated and aligned with business goals. * Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models. About You: Minimum Qualifications: * Have a graduate degree (masters or PhD) in Operations Research or Industrial Engineering * 3+ years of industry experience using machine learning to solve real-world problems with large datasets * Have strong programming skills in Python and fluency in data manipulation (SQL, Pandas) and Machine Learning (scikit-learn, XGBoost, Keras/Tensorflow) tools * Have strong analytical skills and problem-solving ability * Are a strong communicator who can collaborate with diverse stakeholders across all levels   Preferred Qualifications: * Knowledge of deep learning frameworks and methodologies * Experience in applying machine learning and optimization techniques to solve marketplace problems #LI-Remote 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 $207,000—$218,500 USD WA $198,000—$209,000 USD OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI $190,000—$205,000 USD All other states $173,000—$182,500 USD

Senior Machine Learning Engineer II, Ads Response Prediction

Instacart

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 There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. OVERVIEW As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core 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 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 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 * 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. ABOUT YOU MINIMUM QUALIFICATIONS * 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. PREFERRED QUALIFICATIONS * Experience in ads ranking or auction-based systems (pCTR, bid optimization, ROAS feedback loops, marketplace dynamics). * Hands-on experience with autoregressive sequence models for user behavior prediction, generative retrieval, or transformer-based ranking architectures. * Familiarity with learned representations such as Semantic IDs, product embeddings, or other approaches to reducing feature cardinality and cold-start challenges. * Experience with transfer learning or domain adaptation techniques (e.g., LoRA, adapter-based fine-tuning) applied to recommendation or ranking models. * Publication record in top-tier venues (KDD, WWW, RecSys, NeurIPS, ICML, SIGIR, or similar). * Experience mentoring junior engineers or shaping technical direction for a modeling team. * Familiarity with LLM-driven approaches to recommendation, including prompt-based personalization and AI-assisted model development (AutoML). #LI-Remote 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