Head of Population Simulation Research

Aaru•New York, NY

3d•Onsite

About The Position

Aaru operates at the frontier of predictive intelligence, using AI to simulate and predict human behavior at scale. By generating and deploying instances of artificial intelligence that mirror humans, called agents, Aaru simulates entire populations with unprecedented accuracy. Our partners use Aaru to refine strategic positioning, identify and understand high-value audiences, validate concepts and messaging before launch, optimize pricing decisions, and build a continuously richer understanding of their customers through simulation. We provide organizations with invaluable foresight, empowering them to anticipate outcomes and proactively make the right decisions at the right time, every time. We're a small, dedicated, mission-driven team and we intend to stay that way. We believe the best work happens when exceptionally talented people are given ownership, trust and the space to operate without bureaucratic friction. We work with urgency and intellectual honesty and expect new team members to match our velocity. We seek individuals who thrive at the frontier, who push beyond conventional limits, who bring curiosity and conviction in equal measure, and who want their work to have demonstrable impact in the world. If you're energized by the idea of a small team doing things that feel impossible, let’s build together. The role Simulation research focuses on expanding what simulations can be as well as documenting and verifying that the systems today are worthy of trust. As the Head of Simulation Research, you will set Aaru’s research strategy in collaboration with the founders, conduct original research, and lead a team of researchers. This is a hands-on research leadership role. Someone successful will be a player-coach; they’ll be able to get down in the weeds of day-to-day IC work alongside strategizing for long term plans. You will choose the bets to make, the way experiments are designed, and recruit exceptional people to join. While research at Aaru is exploratory, it’s not unconstrained – good research must eventually change what Aaru can build and measure.

Requirements

Have developed an original research agenda at a frontier ML lab or an environment with an equivalent bar for rigor and ambition.
Are equally comfortable forming a research strategy, designing an experiment, writing code, and inspecting individual failures.
Have led researchers or a major technical direction while remaining a direct contributor to the work.
Can turn an important but poorly specified question into a sequence of decisive experiments.
Care more about discovering the truth than preserving an elegant hypothesis or producing a persuasive demo.
Can synthesize ideas across machine learning, behavioral science, statistics, economics, or complex systems without becoming trapped by one discipline’s conventions.
Communicate results plainly to researchers, engineers, customers, and company leadership—including negative results and inconvenient limits.
Want to build in person, in New York, at high speed.

Nice To Haves

Work in multi-agent systems, LLM behavior, post-training, evaluation, synthetic environments, or model-based experimentation.
Work in computational social science, behavioral modeling, economics, psychometrics, causal inference, or adjacent fields.
A record of research that changed a model capability, product decision, or scientific understanding—not merely a benchmark score.
Experience recruiting and mentoring unusually strong researchers and research engineers.

Responsibilities

Define and continuously refine a coherent research agenda for individual behavior, group dynamics, and population-level validity.
Choose a small number of high-leverage bets and establish the experiments, baselines, and decision criteria needed to evaluate them.
Personally design and run experiments, build prototypes, analyze model behavior, and turn ambiguous findings into the next testable question.
Develop new methods for agent construction, memory, interaction, behavioral calibration, population synthesis, and uncertainty estimation.
Build evaluation frameworks that compare simulated behavior with real observations and reveal where apparent success does not generalize.
Establish high standards for reproducibility, falsifiability, negative results, and honest communication of limitations.
Partner closely with Simulation Engineering. Hand off validated methods with evidence, implementation insight, known failure modes, and clear production hypotheses.
Hire, mentor, and lead a small team of exceptional researchers and research engineers.