🛠 Tech Stack

💼 About This Role

You'll tackle fundamental problems in knowledge representation, temporal reasoning, and semantic compression for organizational superintelligence. You'll design memory infrastructure that reasons across time, causality, and context, and publish research findings to the broader community. This role offers the chance to build systems that predict events before they happen.

🎯 What You'll Do

• Build LLM-powered information extraction pipelines for entity-relationship representations
• Develop memory consolidation algorithms using multi-observation validation
• Design temporal knowledge graph architectures for organizational state
• Create graph attention mechanisms for causal query reasoning

📋 Requirements

• 5+ years building novel systems in ML, NLP, or knowledge graphs
• Deep knowledge of knowledge graphs, graph neural networks, or temporal reasoning
• Strong ML and NLP foundation in information extraction or entity resolution
• Proficiency in Python and modern ML frameworks (PyTorch preferred)

✨ Nice to Have

• Graph databases (Neo4j, TigerGraph, Neptune) experience
• Information theory, compression, or temporal data structures background

🎁 Benefits & Perks

• 💰 Base Salary $150K-$300K
• 📈 Equity 0.3%-2%
• 🏥 Comprehensive Health Coverage medical, dental, vision
• 💪 Wellness & Productivity Stipend $2,500/month
• 📚 Learning & Growth Budget for conferences and courses