About this role

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.
Developing scalable feedback and reward modeling approaches for closed-loop learning (RFT).
Building LLM-based evaluation frameworks to improve the quality and reliability of generative and agentic systems.
Researching techniques to deploy LLMs in high-traffic, latency-sensitive production environments.
Working on graph data management and knowledge discovery over one of the world’s largest grocery catalogs.
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
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

Machine Learning Engineer, PhD Intern

Key skills