The Vision: System-Level Optimization

Most current RSI work is highly LLM-centric, treating model weights as the sole unit of improvement. Every step inherits the massive cost of a training run, and compounding progress arrives late. We take a whole-system view: the LLM is just one component of a larger reasoning system that includes code, prompts, search strategies, and tool use. We are building a self-optimizing optimizer—a system where every task it tackles supplies the signal needed to optimize its own orchestration code.

As an AI Scientist, you will propose, explore and hands-on build the core algorithms for our self-improving reasoning engine. You will push the frontier on the data-efficient methods that allow our system to learn how to reason – how to probe LLMs, extract their hidden knowledge, and synthesize fragments into reliable, complex answers.

You are a good fit if you:

• Have a deep, first-principles understanding of LLM reasoning, failure modes, and understand their “quirks” through experience.

• Are an expert in ML algorithm design, search, or optimization, and know the limitations of common LLM-training methods.

• Excel at designing novel, data-efficient methods for discovering optimal, task-spec