About Aaru
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.
About Product Engineering
Product Engineering turns Aaru's simulation capabilities into products that customers can use independently and repeatedly. The team builds on Aaru's shared platform and simulation systems to create the workflows, interfaces, integrations, and decision-ready artifacts that make a technically sophisticated system feel clear and dependable.
The function is designed around durable product domains rather than a queue of disconnected features. Those domains may include simulation setup, question and experiment types, follow-up and continuous simulations, analysis and reporting, reusable customer templates, collaboration, and external integrations.
Product Engineering is not a thin presentation layer and it is not a forward-deployed services team. It owns end-to-end product outcomes, while working through explicit interfaces with Platform Engineering, Simulation Engineering, Research, Product, Design, and Deployment.
How we work
We begin with the decision the user is trying to make, not with a feature request. We seek direct evidence, reduce the problem to its essential uncertainty, and build the smallest system that can resolve it. We move quickly, but we preserve the foundations required for reuse and long-term ownership.
For AI-native products, product quality and model quality are inseparable. Latency, interaction design, evaluation, orchestration, permissions, reliability, and the behavior of the underlying models all shape the user experience. The manager must create a team that can reason across these layers without losing accountability at the boundaries.
Management at Aaru is an active technical discipline. The goal is not to become the routing layer for every decision. The goal is to give strong people enough context, clarity, and feedback that they can own increasingly difficult decisions themselves.