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 Since 2012, Instacart has been focused on making grocery delivery convenient, affordable, and accessible to everyone.

We bring fresh groceries and everyday essentials to customers across the US and Canada from nearly 55,000 stores across 5,500 markets. Our mission is to create a world where everyone has access to the food they love, and to achieve that goal, we innovate in a wide range of areas including e-commerce, advertising, and fulfillment. Machine learning is central to how we build intelligent shopping experiences at Instacart.

We use machine learning and Internet-scale data to elevate customer experience, improve efficiency, and reduce cost. D. students to join our fast-moving ML teams and work on high-impact problems at the intersection of LLM research, large-scale ML systems, and real-world e-commerce applications.

ABOUT THE JOB Based on your passion and background, you may choose to work in a few different areas: Query understanding: Using cutting-edge AI and LLM-based techniques to understand user intent, refine queries, and support downstream retrieval and ranking. Search relevance and ranking: Improving search relevance by incorporating signals from user behavior, catalog knowledge, and generative models, including hybrid retrieval and ranking systems. Generative recommendations: Pushing the boundaries of where generative and traditional models intersect across retrieval and ranking systems; developing scalable feedback and reward modeling approaches for closed-loop learning (RFT).

, LLM-as-a-Judge , self-critique) to improve the quality and reliability of generative and agentic systems. Low-latency and scalable LLM systems: Researching techniques to deploy LLMs in high-traffic, latency-sensitive production environments, balancing quality, cost, and latency through cascading, distillation, and selective generation. Knowledge graphs: Working on graph data management and knowledge discovery over one of the world’s largest grocery catalogs, and integrating structured knowledge with LLM-based reasoning and natural language interfaces.

Sequence modeling: Building temporal models for user behavior prediction.

Minimum Qualifications

D. student in computer science, mathematics, statistics, economics, or related areas.

Machine Learning Engineer, PhD Intern (Fall)