How to apply for Member of Technical Staff, AI Digest

About Sage

Sage is a unique organization focused on forecasting AI progress and its societal risks through technical tools and visual explainers. Unlike typical AI companies, they prioritize understanding and communicating AI's trajectory and potential dangers, making them appealing for those interested in AI safety and responsible development. Their work on the AI Village project represents cutting-edge research into long-horizon agentic capabilities.

About the role

This role involves hands-on development of the AI Village project, focusing on building and studying AI agents with advanced memory systems and interaction design. You'll be responsible for scaling the platform infrastructure, designing new agent challenges, and creating evaluation frameworks to analyze performance. This position is impactful because it directly contributes to understanding frontier AI capabilities and their implications.

A typical day

A typical day might involve implementing memory system improvements for AI agents, designing new interaction scenarios to test agent capabilities, and analyzing performance data from evaluation frameworks. You'd collaborate with the team on scaling the AI Village platform infrastructure while contributing to research on long-horizon agentic behavior and its implications.

Who Sage is looking for

• Has practical experience developing AI agents with memory systems (e.g., using frameworks like LangChain, AutoGPT, or custom architectures)
• Demonstrates ability to design and implement interaction systems for AI agents (e.g., multi-turn conversations, tool usage, or planning capabilities)
• Shows experience scaling technical infrastructure for AI systems (e.g., distributed computing, cloud deployment, or performance optimization)
• Possesses skills in creating evaluation frameworks for AI performance (e.g., designing metrics, running experiments, or analyzing agent behavior)

Tips for this application

• Highlight specific projects where you built AI agents with memory systems - describe the architecture and challenges you solved
• Demonstrate your understanding of long-horizon agentic capabilities by discussing relevant research or projects
• Showcase infrastructure scaling experience with concrete examples (e.g., handling increased agent complexity or user load)
• Include examples of evaluation frameworks you've designed, specifying what metrics you used and why
• Reference Sage's mission in your application materials, showing how your work aligns with their focus on AI progress understanding

What to cover in your cover letter

['Your direct experience with AI agent development, particularly memory systems and interaction design', "Examples of technical infrastructure you've built or scaled for AI projects", 'Your approach to evaluating AI performance and designing assessment frameworks', "Why Sage's mission of understanding AI progress and risks resonates with your professional goals"]

Research before applying

• Explore Sage's AI Village project and their published work on AI forecasting
• Review their visual explainers and tools to understand their communication approach
• Study their mission statement and how they frame AI progress and risks
• Look into their team's background and previous publications or projects

Likely interview topics

Based on the job description, expect questions about:

• Technical deep dive on memory systems for AI agents - architecture choices and implementation challenges
• Designing interaction systems for long-horizon agent capabilities
• Approaches to scaling the AI Village platform infrastructure
• Methods for evaluating agent performance and designing meaningful challenges
• How your work contributes to understanding AI progress and potential risks

Common mistakes to avoid

• Focusing only on general AI/ML experience without specific agent development examples
• Not demonstrating understanding of memory systems or interaction design for agents
• Applying with generic infrastructure experience unrelated to AI agent scaling