AI Research Scientist 4, Generative Models, Recommender Systems

What you'll do

• Translate LLM and generative model research into concrete opportunities for the promotional assets problem space.
• Apply state-of-the-art techniques to improve summarization quality and build LLM-based evaluators.
• Lead end-to-end ML development including research, model training, and evaluation for content generation.
• Optimize generated content for member impact by coupling content generation with recommendation systems thinking.
• Partner with engineers to integrate machine learning models into business applications and platforms.
• Translate complex technical concepts for technical and non-technical stakeholders to inform strategic decisions.
• Identify high-impact opportunities and define execution roadmaps as a subject matter expert.

What we're looking for

• Strong foundation in Machine Learning and Deep Learning, specifically in Natural Language Processing and Understanding.
• Demonstrated habit of tracking LLM and generative model literature to identify relevant business opportunities.
• Expertise in one or more LLM post-training approaches.
• Ability to lead end-to-end ML development including research, model training, and evaluation.
• Ability to translate complex technical concepts for both technical and non-technical stakeholders.
• Research experience in summarization or generated-text evaluation (e.g., LLM-as-judge methods) (preferred).
• Background in vision models such as diffusion models (preferred).
• Experience with recommendation systems, personalization, or other member-facing optimization systems (preferred).