Machine Learning Engineer, PhD Intern (Fall)

Instacart is transforming the grocery industry by making grocery delivery convenient, affordable, and accessible. They are seeking talented Ph.D. students to join their machine learning teams to work on high-impact problems in areas such as query understanding, search relevance, and generative recommendations.

Responsibilities:

• Using cutting-edge AI and LLM-based techniques to understand user intent, refine queries, and support downstream retrieval and ranking
• Improving search relevance by incorporating signals from user behavior, catalog knowledge, and generative models, including hybrid retrieval and ranking systems
• 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)
• Building LLM-based evaluation frameworks (e.g., LLM-as-a-Judge, self-critique) to improve the quality and reliability of generative and agentic systems
• Researching techniques to deploy LLMs in high-traffic, latency-sensitive production environments, balancing quality, cost, and latency through cascading, distillation, and selective generation
• 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
• Building temporal models for user behavior prediction

Requirements:

• 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
• 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