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 work is organized around durable product domains rather than a queue of isolated 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.
This is not a thin frontend role. Product Engineers own user outcomes across the stack. A project may begin with observing a customer's decision process, continue through product and systems design, require new backend or orchestration primitives, involve careful evaluation of model behavior, and end with a measured production rollout.
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. Product Engineers are expected to reason across these layers rather than treating model behavior as somebody else's API.
We prefer clear ownership and small teams. The person closest to a problem should have the context and authority to make decisions, while documenting enough that the rest of the organization can understand and challenge them.