Prediction Researcher

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.

Prediction Research builds systems that estimate future or otherwise unknown outcomes from data. The team's primary object is the population-level outcome: given a population, a question, and the relevant context, what aggregate result should we expect, how uncertain should we be, and how should that estimate change when the conditions change?

Some problems are best solved with structured statistical or machine-learning methods. Others may benefit from language models, retrieval, tools, explicit decomposition, simulated agents, or a combination of these approaches. The team's job is not to assume that the most complex method is best. It is to determine which information and method produce genuine predictive signal beyond strong, simpler baselines.

Prediction Research is not prompt engineering and it is not a speculative forecasting exercise. It is empirical predictive science. A prediction of 60 percent should resolve near 60 percent under the conditions where it is made. Improvements must survive temporal holdouts, new populations, changing environments, and prospective outcomes.

As a Prediction Researcher, you will own difficult, open questions about aggregate human behavior and future outcomes. You will formulate hypotheses, construct or curate datasets, build predictive methods, design evaluations, inspect failures, and communicate what the evidence supports—including when a result is null, unstable, or less useful than a simple baseline.

Your work may combine structured data, statistical learning, probabilistic modeling, language models, retrieval, tool use, and explicit agent simulation. You will be expected to choose methods based on the problem and evidence rather than on novelty. A strong result is not merely a lower benchmark score; it is a predictive improvement that remains calibrated, survives honest holdouts, and matters for a real decision.

You will work closely with Population Research, Evaluation Research, Simulation Engineering, Product Engineering, Data, and Deployment. Validated methods should become reproducible systems with clear limits, not remain isolated notebooks or research demos.

• Own a high-value research question in forecasting, aggregate behavioral prediction, calibration, conditioning, subgroup decomposition, drift, or agentic prediction.

• Turn ambiguous questions into falsifiable hypotheses, strong baselines, appropriate datasets, decisive experiments, and explicit criteria for success or stopping.

• Build prediction methods from real-world records such as transactions, product usage, event histories, operational data, market data, surveys, customer data, and longitudinal outcomes.

• Combine language models with structured data, retrieval, tools, quantitative models, and inference-time reasoning when the combination produces measurable value.

• Develop estimates of population behavior and determine how those estimates vary with attributes, prior behavior, information exposure, environment, time, and intervention.

• Design temporal holdouts and prospective tests that use only information available at the time a prediction would actually have been made.

• Measure calibration, proper…