Machine Learning Engineer, PhD Intern (Fall)

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. A few examples:

• We build state-of-the-art models powering Search, Discovery, and Ads, combining generative AI and traditional machine learning to create best-in-class recommendations
• We build rich product and knowledge graphs from catalog data imported from hundreds of retailers, applying them in recommendations and other user experiences
• We redefine traditional domains across the company with AI, such as hyperpersonalized marketing and 0 → 1 meal planning products

We are looking for talented Ph.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 evaluation and AIQA systems: Building LLM-based evaluation frameworks (e.g., 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.

ABOUT YOU

Minimum Qualifications:

• Ph.D. student in computer science, mathematics, statistics, economics, or related areas.
• Strong programming (Python, Golang) and algorithmic skills.
• Solid foundations in machine learning, algorithms, or optimization
• Curious, self-motivated, and comfortable working on open-ended problems

Preferred Qualifications: 

• Ph.D. student at a top tier university in the United States 
• Hands-on experience with generative or traditional modeling frameworks (PyTorch, Tensorflow, vLLM)
• Prior industry or research internship in machine learning or AI
• Interest and experience in translating research ideas into scalable production systems