Machine Learning Engineer, PhD Intern (Fall)

Details

• 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
• 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.
• 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