RE / RS - Foundations, Search

Highlights

• Build agentic search that turns model intent into reliable real-world actions.
• Design retrieval systems that let frontier models reason over global knowledge.
• Advance memory and knowledge access for AI across products and research.

What they offer

• relocation assistance
• equity

Ideal candidate

A first-principles researcher energized by high-risk, high-reward science who cares deeply about grounding models in knowledge and advancing retrieval for frontier AI. You collaborate naturally with scientists and engineers to bring breakthroughs into production.

Requirements

• Proven experience leading high-performance teams of researchers or engineers in ML infrastructure or foundational research
• Deep technical expertise in representation learning, embedding models, or vector retrieval systems
• Familiarity with transformer-based LLMs and how embedding spaces can interact with language model objectives
• Research experience in areas such as contrastive learning, supervised or unsupervised embedding learning, or metric learning
• A track record of building or scaling large machine learning systems, particularly embedding pipelines in production or research contexts
• A first-principles mindset for challenging assumptions about how retrieval and memory should work for large models