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 Simulation Engineering Manager, you will lead a focused team of Simulation Engineers and be accountable for the quality, pace, and operational health of Aaru's production simulation system. Managers at Aaru remain engineers. You will set technical direction, review critical designs, write and debug code when needed, inspect model and system failures, hire exceptional engineers, and develop the people on the team.
What they're looking for
- Two simulations with nominally identical inputs produce materially different outputs. Trace every source of state and randomness, quantify the acceptable variance, and make the result reproducible enough to explain
- Aggregate accuracy improves while performance for an important subgroup deteriorates. Build the diagnostics and release policy that prevent the average from hiding the failure
- A hundred-thousand-agent run is too slow and expensive for the product roadmap. Find the right combination of algorithmic changes, batching, caching, model selection, inference strategy, and approximation without eroding fidelity
- A research workflow works only in one person's notebook. Turn it into a self-serve production path with typed contracts, tests, experiment tracking, guardrails, monitoring, and rollback
- Product Engineering needs a new simulation interaction that violates assumptions in the existing engine. Decide whether to extend the core abstraction, introduce a separate mode, or change the product design
- An incident cannot be attributed because the relevant model, prompt, data, and population versions were not captured. Fix the immediate problem and redesign the system so the class of failure cannot remain invisible
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.
Simulation Engineering owns the path from a promising research result to a production system that customers can trust. Researchers may prove a new method for constructing a population, modeling a world, estimating an outcome, or evaluating...
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