Applied Scientist II, Alexa for Shopping
Core
Develop and optimize LLM/SLM-powered conversational experiences for an AI-driven shopping assistant, focusing on instruction design, context engineering, model fine-tuning, and agentic systems to improve response quality and customer impact.
Role type
Senior Applied Scientist (LLM/Conversational AI)
Builds
LLM agents, automated evaluation pipelines, and multimodal shopping assistant systems
Domain
E-commerce, Conversational AI, Large Language Models (LLM), Small Language Models (SLM)
Deliverable
production ML models
Required skills
Machine Learning, Deep Learning, Natural Language Processing (NLP), Model Fine-tuning, Prompt Engineering, Agentic System Design, Statistical Analysis, A/B Testing, Data Mining
Preferred skills
PhD in CS/ML, Large Language Model fundamentals, Top-tier NLP/LLM publication record (NeurIPS, ICLR, ICML, EMNLP, ACL, NAACL)
Technologies
Python, Java, C++, LLM frameworks, Distributed Computing
Responsibilities
Develop and maintain LLM agents including automated eval pipelines and rubric design; Lead post-training of small language models (SLMs) for low-latency experiences; Design and evaluate agentic architectures balancing quality, latency, and reliability; Perform hands-on analysis of large-scale multimodal interaction datasets; Collaborate with Product and Engineering to translate insights into production systems.
Seniority
Senior, hands-on IC