Humans are unreliable narrators of their own behavior. Memory fails, incentives distort answers, and social pressure warps what people say away from what they actually do. Backed by General Catalyst, Felicis and Redpoint.
About the role
As an Evaluation Researcher, you will own difficult measurement problems at the boundary of machine learning, statistics, behavioral science, and product decision-making. You will define constructs, assemble or create evaluation data, design studies, write analysis and evaluation code, inspect individual failures, quantify uncertainty, and communicate what the evidence does and does not support.
More about this role
Aaru builds simulations of human behavior. Each simulation contains a population of AI agents, each representing a person who could plausibly exist in the real world and capable of making decisions within a modeled environment. Companies and institutions use these simulations to test consequential choices before committing—from product launches and pricing decisions to strategic communications and policy changes.
Building a useful simulation requires more than generating plausible text. Populations must represent real people and groups; predictions must be calibrated; simulations must remain coherent as conditions change; and the product must make the resulting evidence legible enough to support real decisions.
We are a small, in-person team in New York. We work with urgency, high ownership, and intellectual honesty. We expect people to surface inconvenient evidence, change their minds quickly, and carry important work all the way to a result.
Evaluation Research determines whether Aaru's populations, predictions, and end-to-end simulations correspond closely enough to the real world to support consequential decisions. The team defines what should be measured, develops the methods...
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